Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective
Authors: MeiJun Wang
Yu Meng
Zhongwei Qiu
Chao Zheng
Yan Xu
Pengxiaorui
Jian Gao
Venue: NeurIPS 2024 poster (2024)
Keywords: Pedestrian Pre-collision pose
Human pose and shape estimation
Dashcam Perspective
Pedestrian-Vehicle Collision Pose dataset
Primary area: deep_learning_architectures
TL;DR: We construct the first Pedestrian-Vehicle Collision Pose (PVCP) dataset from the perspective of dashcam, and propose a pedestrian Pre-collision Pose and Shape Estimation network (PPSENet).

Abstract: Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video. However, the pose estimation model trained by the existing daily human pose datasets has poor robustness under specific poses such as pedestrian pre-collision pose, and it is difficult to obtain human pose datasets in the wild scenes, especially lacking scarce data such as pedestrian pre-collision pose in traffic scenes. In this paper, we collect pedestrian-vehicle collision pose from the dashcam perspective of dashcam and construct the first Pedestrian-Vehicle Collision Pose dataset (PVCP) in a semi-automatic way, including 40k+ accident frames and 20K+ pedestrian pre-collision pose annotation (2D, 3D, Mesh). Further, we construct a Pedestrian Pre-collision Pose Estimation Network (PPSENet) to estimate the collision pose and shape sequence of pedestrians from pedestrian-vehicle accident videos. The PPSENet first estimates the 2D pose from the image (Image to Pose, ITP) and then lifts the 2D pose to 3D mesh (Pose to Mesh, PTM). Due to the small size of the dataset, we introduce a pre-training model that learns the human pose prior on a large number of pose datasets, and use iterative regression to estimate the pre-collision pose and shape of pedestrians. Further, we classify the pre-collision pose sequence and introduce pose class loss, which achieves the best accuracy compared with the existing relevant \textit{state-of-the-art} methods. Code and data are available for research at https://github.com/wmj142326/PVCP.

Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective
Meijun Wang', Yu Meng's Zhongwei Qiu', Chao Zheng', Yan Xu', Xiaorui Peng', Jian Gao
University of Science and Technology Beijing
Alibaba DAMO Academy Northwest University
drumj @xs. ustu.uttbee... edu. cn, myu@ustb.. edu en, qiuzhongwei wttph/owwwe..gw@lllbbbbbb..e qzw@alibaba-ing com fminiflash, b20160225, pxrw] ustb. edu. en, gaojian1@stumali nwu. edu, en
Abstract
Pedestrian pre-collision pose one of the key factors determine the degree of pedestrian-vehhe veehcle injury in collision. Human pose estimation algorithm is effective method to estimate pedestrian emergency pose from accident video. However, the pose estimation model trained by the existing daily human pose datasets has poor robustness under specific poses such as pedestrian pre-collision pose, and it is difficult to obtain human pose datasets in the wild scenes, especially lacking scarce data such as pedestrian pre-collision pose traffic scenes. In this paper, collect pedestrian-vehicle collision pose from the dashcam perspective of dashcam and construct the first Pedestrian- Vehicle Collision Pose dataset (PVCP) in semi-automatic way, including 40k+ accident frames and 20K+ pedestrian pre-collision pose annotation (2D, 3D, Mesh). Further, we construct a Pedestrian Pre-collision Pose Estimation Network (PPSENet) to estimate the collision pose and shape sequence of pedestrians from pedestrian-vehick accident videos. The PPSENet first estimates the 2D pose from the image (Image Pose, ITP) and then lifts the 2D pose to 3D mesh (Pose to Mesh, PTM). Due to the small size the dataset, we introduce pre-training model that learns the human pose prior on a large number of pose datasets, and use iterative regression to estimate the pre-collision pose and shape of pedestrians. Further, pose sequence and introduce pose class loss, which achieves the best accuracy compared with the existing relevant of- art methods. Code and data are available for research at http:://gihhub..omm/w.e 143206/PC222222P
classify the pre-collision
Introduction
Pedestrian pre-collision pose refers to the emergency actions pedestrians take when facing potential hazards before collision accidents, affecting both the severity of pedestrian injuries and subsequent injury assessments (I). Using collision simulation software reconstruct redestrian-vehiel accidents a popular and effective method for analyzing pedestrian injuries 22 3 4 5) However, the current
5 manual measurement of pose angles from accident images, the former cannot represent the posture of pedestrians in real accidents, and the latter is inefficient. Computer vision-based pedestrian pose estimation methods can directly estimate pose information such as joint positions or limb angles from images in real-time 8 9 TO 12 I.S). Existing pose estimation methods are trained and applied on multiple datasets for various scenarios, adapting to different downstream tasks. However, unlike common poses, pedestrian pre-collision poses traffic scenes are specific, with differences spatial-
input for initial pose still relies predefined gait sequence templates (6
"Correspondngg author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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temporal characteristics and challenges such as dynamic backgrounds, sudden scene changes, and occlusions of lower limbs (5). Directly applying existing algorithms to pedestrian pre-collision pose estimation in traffic scenarios does not achieve perfect adaptation effects.
Training proprietary network with specific datasets can effectively improve the pose estimation performance of the network in that scenario. Precise 2D pose annotations can be obtained through time and hanpower-intensive efforts, indoor motion capture (Mocap) systems (16) 17) utilize markers and sensors to acquire high-quality 3D motion data. However, acquiring ground truth (GT) 3D joint positions in-the-wild is nearly impossible (12). Existing
datasets either do not contain human pose (18) 19) labels or only include limited movements of daily activities (20) 21 14). Furthermore, training a model using large amount of data is costly, and video data of pedestrian- vehicle collisions belongs to small sample scarce data in traffic scenes, making dataset collection
difficult. Dashcams or public surveillance devices are the only sources of data (22 23 24 (5), further constrains the approaches to dataset creation, thereby enhancing the difficulty and complexity of producing such datasets.
In this work, we constructed a Pedestrian-Vehicl Pre-collision Pose (PVCP) dataset and proposed a simple framework for Pedestrian Pre-collision Pose and Shape Estimation (PPSENet) in collision accident videos. We collected dashcam videos and used existing pose estimation algorithms to obtain rough 2D keypoints and 3D mesh initialization results, followed by manual correction using specialized annotation tools. Specifically, we designed an SMPL annotation tool (25) to align the initial results with image contours, resulting in approximately 40K+ frames of accident images and 20K+ instances of pedestrian emergency poses with both 21) and 3D annotations. Our PPSENet estimates the 2D pose from images (Image to Pose, ITP) and lifts the 2D pose to the 3D mesh (Pose to Mesh, PTM). We used a pre-trained model (12) to capture prior knowledge of human actions and employed iterative regression [T6 27) to estimate pedestrian pre-collision poses and shapes. Additionally, we classified emergency poses and introduced pose class loss, achieving superior accuracy compared to existing methods.
The main contributions of this paper are summarized below:
We constructed a pedestrian pre-collision pose dataset, PVCP, by collecting dashcam videos of dedestrian-vehicke collisions. Through algorithm initialization and manual annotation, we obtained rich pose representation annotations, including 2D, 3D keypoints and SMPL. mesh.
We propose two-stage pedestrian pre-collision pose and shape estimation network, PPSENet, which first estimates the 2D pose from the image and then lifts the 2D pose to the 3D pose. A pretrained encoder with pose estimation and iterative regression decoder are combined, and introduce a collision pose class loss.
Our framework achieved promising results on the PVCP dataset, outperforming other methods of human pose estimation. This provides both data and algorithmic support for pedestrian pre-collision pose estimation and active safety protection for pedestrians.
2
Related Work
Pedestrian Pre-collision Pose. Pedestrian pre-collision pose is crucial for studying collision damage, as the initial posture at the time of impact directly affects the severity and nature of the injuries (28) 6 22 30 3 31). Early studies estimated collision poses by collecting post-accident data from pedestrians and vehicles (32). Cadaver tests (33) became effective for biomechanical damage studies but are limited by ethics, sample size, and high costs. Currently, collision simulation software the most convenient and effective method assess damage under various poses (2)
(5). 4 5) differing
However, initial collision poses are often fixed templates or simple categories (6) 2 significantly from real pre-collision poses. One method to obtain pre-collision poses is using motion capture in virtual environments with volunteers (34), but this is limited by device constraints and lack of real danger. Another method captures collision sequences from real accident videos, manually measuring posture angles or adjusting dummies to match real collision poses (33). This method is closer to real accident scenarios but labor-intensive, time-consuming, and lacks standardized testing, limiting its use to single accident reconstructions. With advances in deep learning and computer vision, some research has employed human pose estimation algorithms to automatically extract collision poses from accident images (15), providing new approach to acquiring pre-collision poses
3

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Table 1: Comparison of datasets on Accident Warning, Traffic Scene and Pedestrian Pose. 'V represents the vehicle perspective, 'M' represents the monitoring perspective, 'D' represents dynamic background and 'S' represents a static background.
Type
Datavet
Year
Perspective
Background
Dutection
Track
Depth
Pose
Shape
Class
Frame
DAD
2016
V(D Biux)
ShanghuiToch
2017
('Maskk
Accident Warning
A3D 23
2019
V
D
VCD Boox)
>128k
DADA 1b
2019
V
VOD Biox)
>650k
Bbox)
2013
<DD Rhux)
V
Ciyscupes 18
2015
V
D
((Mark)
25k
Traffie Scone
City ClyPenons 40
2016
V
VCD Biox)
>5k
2017
VMM
Bbox)
Nuscenes
2019
VOD Boox)
>35
MSCOCO/20
2014 2014-2017
Duily scene
V2D Boox)
(22
>100k
Human 6M hb
2014
M
V(DD Bhox)
((2D/DD)
>500
Pedestrian
hand-held
((D))
Accident Vidro 3
2020
VMM
Pedx F4
2018
M
(Maak)
V(20/5D)
>
Ours
PVCP
2024
V(Dacheam)
INS
/DD Bbox)
v(203D)
-8%
in real accidents. Rapid and accurate acquisition of pedestrian pre-collision poses supports research on collision damage and active safety protection.
Human Pose Estimation. Human Pose Estimation (HPE) is fundamental task of computer vision, which aims obtain human pose information such joint positions and angle from images and videos (35). can be simply classified into 2D human pose estimation and 3D human pose estimation. 2D HPE regresses pixel coordinates (x, y) of joints, while 3D HPE includes depth to obtain three- dimensional coordinates (I, =) 7 36). Though 3D coordinates can be regressed directly from 40:
41, using 2D pose intermediate supervision before lifting 3D often achieves higher accuracy I 42). Additionally, the SMPII (Skinned Multi-Person Linear Model) (43)
images 37 38
39
has gained popularity for providing pose and morphological information, along with prior knowledge of body structure, avoiding issues with limb length changes (44) 12 27). This rotation based model is particularly useful in biomechanical research [15] 45), which benefits the study of pedestrian emergency poses. In our research, we used 2D-oo-3 lifting to estimate the pre-collision pose and shape of pedestrians from real accident videos.
Accident and Pedestrian Datasets. Collecting 3D pose datasets complex traffic scenes poses challenges due to the dynamic environments and uncertain pedestrian poses. While existing large- scale datasets focus on 2D (20) 51 (52), 3D pose datasets are often confined to indoor settings using Motion Capture (Mocap) systems(16] [7) estimated via Inertial Measurement Units (IMUs) for outdoor (2se Models trained on indoor datasets do not adapt well to other in-the-wild tasks. Advanced pose estimation methods can generate pseudo-datasets construct 3D pose datasets in the wild. Although pseudo-3D labels from sumi-automatic (3) or fully-automated methods (5)) less accurate than Mocap data and may contain noise, they significantly improve regression-bant methods 56). Using semi-automatic method, we collected dashcam videos of collisions to create pedestrian-vethe vehicle collision pose dataset, offering rich annotation information and contributing to pedestrian protection tasks. Table I Thighlights its advantages over other datasets.
3
PVCP Dataset
3.1
Data Collection
Dashcams public surveillance devices are the main sources of crash data (22 22]223]2444 (5, Dashcam views dominate vision-based Traffic Accident Anticipation (TAA) datasets due to the high potential for collision avoidance through vehicle control (57). Our PVCP dataset are all derived from the vehicular perspective of dashcam, and videos are sourced from two primary origins. Similar to previous works (47 22 23 24 15), we collected videos of pedestrian and vehicle collisions from online platforms such as YouTube, using peedestrian-ehhie collision" as a keyword.
addition,
small number of videos are derived from existing open-source traffic datasets 447 22 23), which were primarily developed for tasks related to driver attention and the prediction of sudden accidents. All of the collected videos were reduced to individual accident footage, recording complete pedestrian

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MDv
Iesz
2D
3D
Mteh
Toac Net
2D Powe Not
(b) Pre col collission posa class definition
kyt
Shape
Heffoe
After
20 Iniitalizttio Souut
SMPL Annstation Tool
(a) PVCP dataset anmotation pipeline
Pose and Shape parameters distrbuttinn
Figure I: (a) PVCP dataset annotation pipeline. (b) Pose class definition. Different colors are used represent different pose types. (c) Dataset attribute distributions. Utilizing UMAP(58), the pose parameters 0 E RR x72 and shape parameters shape 3 E R 10 are reduced to a wo-dimennioonll IxNx embedding space (no unit), with coordinates along the x and y-axes, respectively.
and vehicle collision, resulting in 209 pedestrian-vehicle accident videos, totaling 42,511 frames of images and about 19,533 pre-collision poses.
3.2 Data Annotations
For the application of eetesttrianvehiicl collision accident reconstruction and vehicle active protec- tion in traffic collision scenarios, PVCP provides rich pedestrian pose representation, including pedestrian clipping images, pedestrian Bounding Box, track id, 2D and 3D keypoints and SMPL mesh label. The entire annotation pipeline is shown in Figure Ta).
Pedestrian Pre-collision Pose. Manually annotating human keypoints is tedious and labor-intensive. We address this by initializing the annotation process with pose estimation models and refining the results manually. First, we identify collision-involeed pedestrians each video using a tracking network (59) and manual filtering, followed by pose annotation. We use 15 keypoint representation similar to the JHMDB dataset (60) for efficient pose depiction. ViTPose (10) provides rough 2D pose annotations, which we manually adjust for accuracy. For occluded limbs, we estimate positions to complete the 2D skeleton, excluding body parts beyond the frame. Annotating 3D human poses in-the-wild remains challenging with images as the only source. Unlike Pseudo-GT annotators (T. (2),, we use SPIN IT to initialize predictions from cropped images, then refine the SMPL model parameters 0 E 21X3 and E R using our specially designed SMPL annotation tool (25) for better pixel alignment. This yields mesh pose annotations M(0, 3) E 12800033 for pre-collision pedestrians. Finally, we apply the pre-defined joint regression matrix J Jx6900 (44) to obtain 3D keypoints X3D E R 2x3 from J (M(O0, B)), where J = 17 (16), as shown in Figure l a).
Pedestrian Motion Class. Throughout the course of -eehete collision event, pedestrians often undergo series of rapid evasive action changes short period. Effectively and accurately distinguishing and predicting these imminent changes is crucial for the proactive safety features of vehicle driving systems. Through the observation and analysis of collected accident videos, as shown in Figure (b), we categorize pedestrian behaviors in collision sequences into four types:
Normal pose: Represents the pedestrian's ' pose under normal, non-emergency conditions. This includes upright body pose and natural stances, reflecting the general behavior of pedestrians when not faced with emergencies.
Run pose: Characterized by the pedestrian's 's body leaning forward with rapid alternation of arms and legs, this pose is active measure prevent vehicle collisions. It reflects preemptive action to swiftly move away from potential threats, serving as a strategic pose to avoid accidents.
Avoid pose: This pose is adopted by pedestrians upon detecting an imminent collision or other emergency situations. includes potential actions such as jumping, quickly turning around, and dodging, reflecting the emergency response of pedestrians when recognizing potential danger.

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Expe2a
Image to Poso
PVCP Perletriinn Collisien Pose
Delent
Ispat Image
Coop Bock
Pesture Extraction
20 Skeletuss
Pro- training
SSMPL
Poso to Mosh
pose
Head pres
Spatial
Temspnal
M (0,8)
L Motion
Ahntton
Attenton
stape
stage
B
Tempooa Abmiton
Spotial Attenton
Eleed clas:
SultKar
Cicass
M-
20 Staletons
MotuatlER Enootee
Iteratin Decoder
Outper Mesk Sequertill
Loss Fandion
Figure 2: The overview the PPSENet. It consists of two stages: image to 2D pose (ITP) and 2D pose to 3D Mesh (PTM).
Collision pose: Represents the pose of a pedestrian post-collision with a vehicle. encompasses possible actions such as losing balance, falling, and sustaining injuries, reflecting the change in the physical state of pedestrians after a collision.
The sequences of two behavior annotations are illustrated in Figure 1 [b). It is important to note that complete collision incident does not necessarily encompass all four types of behavior, nor is there fixed sequence. Because some accidents do not result in a final collision due to timely measures taken, and some pedestrians may not even be aware of the approaching vehicle. To distinguish between the four types, we employ four-digit one-hot encoding for pose annotation. For some critical changes pose, we use three annotators and take the majority's annotation result as the final Ground Truth.
3.3 Dataset attribute distributions
PVCP dataset contains the pose sequences during pedestrian collision. We visualized the pose and shape distribution of SMPL labels in PVCP dataset and compared them with those in commonly used and influential datasets in human pose estimation, including indoor MoCap pose dataset Human3.61 M.6M (16), in-the-wild dataset PW3D (1 a pseudo-3D labels dataset MSCOCO (2)) and
a traffic scenario pedestrian dataset PedX (14 are shown in the Figure CT.. The distribution of pose parameters in our dataset is comparable to PW3D and significantly larger than that of everyday poses in MSCOCO. Our dataset contains over 200 individual collision pedestrians, and its shape parameter distribution more extensive than other datasets that use a limited number of actors, such as 5 actors in the Human3.6M dataset. The most similar PVCP is PedX, but its distribution is sparse due to small number of poses. sum up, our PVCP has good generalization in pose and shape parameters, representing pedestrian pose under variety of collision conditions. Compared to existing pose datasets, PVCP demonstrates significant differences and advantages in terms of scene specificity, action space variation, and temporal continuity.
4 Network Architecture
In this section we present a pipeline for pedestrian Pre-collision Pose and Shape Estimation (PPSE), shown in Figure 2 adopts a top-down two-stage strategy. Instead of estimating the posc of all pedestrians in the whole images, we pre-select the Bbox of collision pedestrians obtained by the detect and track networks (59). The crop of single pedestrian network to extract the image features of the collision pedestrian and estimate the pedestrian's 's 2D pose. Then, the 2D pose was lifted to 3D mesh through the Pose Mesh (PTM) network.
input into the Image to Pose (TTP)
4.1 Pedestrian Pre-collision Pose and Shape Estimation
Image to Pose. Estimating 2D human pose from images a basic and mature task, and many works have achieved very effective results in different datasets. We take image frame I and corresponding
5

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Bbor as input, simply use ResNet50 (63) as the backbone of feature extraction, and use transposed convolution and heat map regression head H. as the 2D pose estimation network, which also the classic paradigm of 2D pose estimation (8).
Pose to Mesh, PTM is network architecture for 2DD-to33D lifting, we take the 2D pose sequence 12D E 1T''XXCC'n as input. First, Dual-stream Spatio-temporal Transformer (DSTformer) is used as an encoder to extract the spatio-temporal features of the pose sequence Fm. E RT*JxC,, Then we designed an iterative regression decoder to obtain pose parameters and shape parameters respectively, At the same time, we added an additional regression head of pose class to coordinate with pose class loss further improve the precision of pose regression. Where T represents the length of the pose sequence and J represents the number of pedestrian joints. J = 17 takes the Human3. 3.6 dataset (16) joint format and we generate additional nodes from P24 E 1
Inspired 12). PTM uses the backbone of an pre-trained model, MotionBERT (2), and combines PVCP dataset to estimate pedestrian pre-collision pose in collision scenarios. MotionBERT is pre-training model human motion representations. Firstly, it learns the prior knowledge of human motion poses under the training of large number of datasets, which is suitable for the further improvement of small datasets such as PVCP and special pose types such as pre-collision poses. Secondly, its training mode simulates the detection results by randomly masking and adding noise 2D pose sequences. In the same way, we train the situation of vehicles shielding pedestrian's 's lower limbs in collision environment. This encoder stacks spatial and temporal Multi-Head Self-Attention (MHSA) blocks in different orders to form two parallel computation branches:
F a'sT (((((1))) ats (F((T(FF')))
(2) Where E II....,,, M and M represents network depth. S; and T; represent Spatial MHSA and temporal MHSA of different depth layers, respectively. Adaptive fusion weights asT, ars fuses the output features of the two branches using adaptive weights predicted by an attention regressor. Wj denotes linear layer, @ denotes concatenation.
ggr, OTS = softmaz(W) (T @ (F
We divide the fusion feature Fm RTxxxC, obtained by DSTFormer into three branches using different linear layers. That is, pose feature F9 E TRCC shape features F3 E IR? Txx and class features F. F.ERT'CC Then the three features are predicted by three different heads, outputting the pose parameter 0 X shape parameter E IR and class probability use the 6D rotation representation to converge the pose parameter more quickly (64). Further, we adopted the idea of iterative regression add the fusion features of pose and form with the predicted results and iteratively output:
RTx4 Here,
@h = (Fo) + 0 3h = WF + Bh+ c= softmar
Where & 1, 1..., N and N represents the number of iterations. We. Wa and W. are three linear transformations heads, @ denotes concatenation, + denotes add.
4.2 Loss Function
The loss function of ITP, defined as the Mean Squared Error (MSE), is applied for comparing the predicted heatmaps A and the ground truth heatmaps H. The heatmap for joint l: is generated by applying 2D Gaussian centered on the kig joint's location.
LITP = A H|_
The PTM loss function consists of three parts: SMPL loss, motion loss, and the pose class loss introduced by us. The final loss function is calculated as:
LPTM = LSMPL. LMMation LCIass
(5)
SMPL loss: The loss function of SMPL-based 3D human mesh usually consists of three parts:
LSMPL XgCo + Aghp + Xn Cnorm
(6)

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Where La = 0
0 Lg
3
Lnorm
+
+||]|
represents pose loss, shape loss
and normalization loss respectively.
The human body a complex rigid structure interconnected between joints, and the continuous frame sequence of human body movements possesses certain temporal characteristics. Therefore, introducing motion loss as
Motion Loss:
Motion = ALCKEAD + Xphr
Where LRP3D 8 X represents the loss of 3D keypoints, L, V vI] represents speed
loss, and V = VX+++1 Xx, V -
Pose class loss: The pedestrian pose of the collision sequence has obvious categories, namely normal pose, running pose, avoiding pose and collision pose as described in Sec. 3.2 the class loss of pose is defined as follows:
CCluss = Aress Entropy (Ö, C?)
(8)
where LCress Entropy (Ö, C) epresents the cross entropy loss between the predicted pose class and the GT pose class. Ao A3, An Ak: Av de are the constants of the balance weight loss.
Experimental and Results
5.1 Dataset, Evaluation Metric and Implementation Details
Dataset. The PVCP dataset consists of over 20K+ pedestrian pre-collision poses, with 19,533 poses annotated with category labels. Subsequently, we selected 164 video sequences as the trainset and 45 video sequences as the testset. To ensure the effectiveness of the pose sequences, only poses with the number of keypoints Napt > 10 were selected, resulting in 15,458 poses for training (Normal:7,912; Rum:5,044; Avoid:2,289 Collision:213) and 5,503 poses for testing (Normal:3,38) Run:1,431; Avoid:631; Collision::8). The entire training process only utilized the PVCP dataset, while the pre-training model weights were obtained from MotionBERT
(2) trained on the AMASS
(17), Human3.6M (16), PW3D (21), and MSCOCO (20) datasets.
Evaluation Metric. We evaluated the estimation of 3D human pose and shape using the following metrics: MPJPE(mm,, J), PA-MPJPE(mm,,) .)). MPVE(mm, J), PA-MPVE(ma...) J.) Further, we test the errors of 14 keypoints (X_14j) shared by 2D and 3D pose representations (21) and 17 keypoints (X_17j) represented only by 3D pose representations (16) respectively.
Implementation Details. PyTorch (65) was used for the entire experimental environment, four NVIDIA RTX 2080Ti GPUs for all training, and batchsize was uniformly set to 32. In the training stage, we only use the images from the PVCP trainset and the corresponding 2D ground truth keypoints as the input of the two models. We first train the ITP network by loading pre-trained model of the MPII dataset (51) and training 40 epoches. For PTM, we use sequence length T = 16 and train 100 epochs in about two hours. In the test stage, in addition to the 2D ground truth keypoints of the testset, we also take the image of the testset as input to the ITP model, and then take the estimated 2D keypoint results as input to the PTM model. The effects of the same PTM training model with two different inputs are compared.
5.2 Effects of Dataset and Pose priors
We first evaluate the effects of PVCP dataset on improving the pedestrians pre-collision pose es- timation. Compared to large-scale human pose datasets, our PVCP dataset is not numerically dominant, so we use a pre-trained model that learns human pose priors and fine-tune it based on that. We ran tests on the original MotionBERT (12) to compare scratch training and the PVCP trainset with together pre-trained models. We take the detected 2D pose sequence (2D Det) as input, and compare the errors of four pre-collision pose (Normal, Run, Avoid, Collision) and all pose (All), As shown in the Table the results of only-pretrain model or only-PVCP trainset are relatively poor. Due to the difference between the pre-collision pose and the daily pose, the error of using only-pretrain model (M Edet = 335.1 1mm) is even worse than that of only-PVCP dataset (M PV Edet = 315. 64mm). When the pre-trained model and the PVCP dataset are trained together, The minimum error (MPV Edet 282.50mm is obtained.
7

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Table 2: Effects of Dataset and Pre-training. Top use detected 2D pose sequences. Bottom GT 2D pose sequences.
Input
Train Set
Pose class
MPVE
PAMPVE
MP.PPE
PAMPJPE,
MPJPE_ 17]
PAMPIPE
Normal
315,94
160.25
272.18
130.72
246.42
121.30
Run
318.29
189.84
274.78
160.35
246.95
145.07
PVCP
Avoid
305.01
159.19
260.31
121.42
232.56
113.21
Collision
347.53
171.2
311.88
145.64
281.35
139.46
All
315.64
168.1
271.91
137.75
245.35
126.92
Normal
347.10
190.17
312.21
134,85
285.02
145.35
152.19
2D
Pretrain
PVCP
Avoid
330.18
189.69
293.76
155.54
264.89
144.38
Collision
334.14
164.32
301.52
133.26
275.28
128,19
All
335.11
188.09
300.80
154.06
274.27
144.12
137.39
Pretrain
Run
253.16
149.99
219.06
124.01
200.19
115.27
PVCP
Avoid
286.85
159.69
246.94
124.86
222.02
14.99
PVCP
Collision
250.58
161.25
222.47
127.37
200.38
120.47
All
Normal
304.65
167.56
260.68
338.49
233,70
126.85
296.75
254.58
163.80
PVCP
PVCP
Avoid
277.51
157.48
234.30
123.02
206.55
113.44
Collision
354.76
178.22
319.56
154.95
287.38
146.83
All
300.04
173.09
256.69
143.73
229.30
130.85
Run
153.45
107.26
131,93
84.02
118.99
77.76
2D GT
Pretrain
PVCP
Avoid
143.32
93.61
122.91
73.45
111.33
68.89
Collision
151.18
91.20
133,60
77.66
124.71
71.06
All
Normal
156.06
103.16
132.74
80,59
120.35
74,92
Pretrain
Run
129.49
89.31
109.93
70.91
100.19
65.70
PVCP
Aveid
127.04
85.36
108.30
65.35
96.74
60.44
PVCP
Collision
All
145.77
97.50
124.04
76.34
112.43
70.87
Table 3: Component of system. Top use detected 2D pose sequences. Bottom use GT 2D pose
sequences.
Input
Pretrain
Iter
Class Loss
Pose class
MPVE
PAMPYE
MPJPE 14j
PAMPJPE_ 14)
MPJPE_ 17]
PAMPJPE_ 17]
All
282.50
163 58
243.59
132.43
222.70
123.33
266.20
225.38
Det
/
All
259.05
143,52
220.39
115.47
200.16
107.03
3
/
All
257.75
144.19
218.61
114.50
198.16
All 145.77
97.50
124.04
76.34
70.87
3
All
145.75
96.69
123.16
75.13
111.90
69.89
GT
141.28
120.16
3
All
140,43
96.43
118.80
25.13
107.47
69.56
When 2D ground truth pose sequence (2D GT) is used as the input, the result without using the pre-trained model relatively poor (M PV Egt = 300. 04mm), and the error after using only-pretrain model is significantly decreased (MPV Eg = 165. (655990mm, because the number of keypoints (2 10) of the 2D GT pose sequences are relatively complete compared with the 2D Det pose sequences, its input is not affected by lighting conditions, background appearance, clothing, and weather conditions. Similarly, when the pre-trained model the PVCP dataset are trained together, The minimum error (MPVE, Egt 145. 77mm) is obtained. This shows that our PVCP dataset has differentt features from ordinary pose, and the pose prior the pre- trained model can effectively promote the precision of the pre-collision pose.
5.3
Ablation Study
In the stage of PTM, we added an iterative decode, which does not directly predict the output once gradually approximates optimal solution with multiple iterations. the same time, an pose classification regression head is added to use class loss supervision. As shown in Table 3 we compared the impact of different components on network performance, loaded the pre-trained model each time, and set the optimal number of iterations to (as shown in Table 4).

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Table 4: Comparison of 2D GT input in different iterations number.
Iter
Pose class
MPVE
PAMPVE
MPJPE 14j
PAMPJPE_LE 11j
MPJPE_IN 17j
PAMPJPE_ 17j
All
141.95
97.43
120.04
75.45
108.63
69.85
All
140.43
96.43
118.80
75.13
107.47
69.56
All
139.96
96.92
118.46
75.19
107.16
69.62
All
140.01
97.10
118.54
75.40
107.27
69.83
All
140.41
97.42
118.89
75.70
107.68
70.14
Table 5: Comparison of state-of-the-art methods on the PVCP testset. denotes that the training
weights provided by the official are used, and denotes the model weights trained together with the PVCP trainset.
Paradigm
Method
Pose class
MPVE
PAMPYE
MPJPE 14j
PAMPJPE
17j
PAMPIPE 17j
Normal
856.87
234.47
731,90
Run
856.10
252.67
732.33
226.45
VIBE 6O
Avoid
777.92
227.16
664.25
216.72
212.21
All
849.09
253.08
725,92
219.55
Stage
Normal
225.99
147.04
193.62
114.35
PPRR
Avoid
210.02
143.88
176.76
109.10
Collision
247.18
167.62
225.96
132.89
226.98
155.72
191.97
119.85
222.34
Run
255.36
181.16
222.33
145.14
Mose2Mesh AS
Avoid
217.97
141.43
191.38
112.35
All
245.88
156.69
218.71
127.41
Normal
294,73
170.10
253.80
137.39
232.74
128.24
149.99
219.06
200.19
Two Stage
MoticaBERT 1
Aveid
286.85
159.69
246.94
124.86
222.02
114.96
Collision
250.58
161.25
222.47
127.37
200.38
120.47
243.59
Normal
272.79
149.02
230.49
117.47
209.99
109.04
Run
226.22
133.45
193.75
109.50
174.47
100.73
(Ours)Ours)
Avoid
251.60
143.52
212.75
109.75
190.00
100.09
Collision
217.68
134.95
201.15
113.10
174.57
105.94
All
257.75
144.19
218.61
114.50
198.16
105.86
Due the difference between 2D Det pose sequences and 2D GT pose sequences in the number and correct position keypoints, when only iterative decode used, the input error of 2D Det decreases significantly PMPVEEe:
282.50mm - 266. 336.20mm), while that of 2D GT decreases slightly (MPVE, Egt = 145.77mm + 145. 145.75mm), which may be because multiple iterations improve the pose regression ability of incomplete pose. When only pose class loss is added, the error reduction space of the 2D GT input is significantly stronger (MPVE, = 145. 75mm + 141. 281mm) than 259. 759.05mm)), possibly because the pose that
that of the 2D Det input (M Edet is complete and correctly positioned at the keypoints has a stronger correlation with the pose class label. Under the combined action of iterative regression decoder and loss function, both 2D pose sequence inputs achieve the minimum error (MPV Edet 257. 77mm, M PV Egt = 140.4. 440.43mm)).
5.4 Comparison with the state-of-the-art
Quantitative comparison. Similar to (69). Table reports results for multiple baselines the PVCP testset using the evaluation metrics described in Sec. 5.1 We compare the classic baseline methods of paradigms: the one-stage method, which involves direct regression from image to mesh, and the two-stage method, which involves regression from 2D pose to 3D mesh. Following (66) (7). the one-stage method, we used ResNet-50 (63) to extract the feature f 22048 of the collision pedestrian clip-off image in each frame. Following (68). We use DarkPose (70) to extract 2D poses COCO format
(20). For MoT (12) and our method, 2D pose is converted from 15 keypoints of JHMDB (60) to the corresponding 17 keypoints of Human3.6 (16) as input. Because the PVCP dataset contains only pose and shape annotations, there a lack of spatial arrangement in the 3D scene. Therefore, the error of MPVE and MPJPE is large, but in PAMPVE and PAMPJPE, our method achieves the best accuracy. In addition, the one-stage method, the effect PARE (67) is relatively excellent, because PARE has optimized the occlusion of pedestrians and is well adapted

---

Figure 3: Qualitative comparison. Left: Comparison with SOTA methods in PVCP testset. VIBE (66) and PARE (67) take images as input, P2M (Pose2Mesh) (68) and MotionBERT take detected 2D pose as input. Right: Output examples of our method in PVCP testset.
to the real situation of the occlusion of pedestrians' lower limbs in the collision scene. In addition, the two-stage method needs to detect the 2D pose first, and the missing keypoints detection caused by occlusion will continue the error to the subsequent Mesh regression stage. However, in the case of single-class pre-collision poses (Run, Avoid, Collision), our method still has great advantages.
Qualitative comparison. A qualitative comparison of different methods is shown in Figure 3 Ignoring the global orientation and position, we manually adjust the Mesh output of different methods to the outline of the pedestrian in the picture, focusing can be seen that our method is most close to the real pedestrian pose the image, and at the same time close to the estimated 2D pose, especially for the most complex pedestrian upper limb (row 4).
comparing the pre-collision pose itself. It
6 Conclusion
In this work, we construct the first Pedestrian- perspective of dashcams, which contains a variety of pedestrian pose representation annotation. At the same time, we propose a framework called PPSENet for the estimation of pedestrian pre- collision pose and shape. Specifically, two-stage method is adopted: first, the pedestrian's 2D pose is estimated from the image, and then the pedestrian's 's 3D mesh is estimated from the 2D pose. Furthermore, adopt the pose prior of the pre-trained model and the idea of iterative regression, introducing the pose class loss to achieve the minimum етrоr on the PVCP testset, effectively estimating the pedestrian pre-collision pose traffic collision scenarios. We hope that this work will offer new insights into human pose estimation and active pedestrian safety protection.
-elestrrian-ehhclee Vehicle Collision Pose (PVCP) dataset from the
Limitations. Due the difficulty of collecting the dataset, the dataset is small in size and lacks real camera parameters, vehicle speed information, global position and direction of pedestrians. Additionally, our task involves two-stage of pose estimation, and the final pose error is largely influenced by the 2D pose estimation results from the first stage. Our method is not real-time at present, because our input is Image and pre-selected Bbox sequence of collision pedestrian targets.
Future Work. From the comparison with state-of oftee--f-hh--rr the-art (SOTA) methods, is evident that methods like PARE (67), which directly estimate 3D meshes of pedestrians from images, also achieve good results. Future work can focus on new improvements in one-stage methods. Furthermore, hope that the introduction of more modal information estimation. The purpose of our work is to provide pose data support for the study of vehicle active and passive protection system, to facilitate subsequent accident reconstruction, pedestrian injury assessment and vehicle structural design.
further improve the accuracy of the
10

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Acknowledgement
This work was supported in part by National Key Research and Development Program of China (Grant No. 201999001,2000) and by the Open Funds of State Key Laboratory of Vehicle NVH and Safety Technology (Grant No. NVHSKI-2021077.
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Appendix supplementallll material
Overview
This supplementary material presents more details and additional results not included in the main paper due to page limitation. The list of items included are:
Details of PVCP Dataset in Sec. A
Details of the Network Architecture in Sec.
Details of Experiment and Results in Sec.
A PVCP Dataset
A.1 Data Collection.
Our PVCP dataset are all derived from the vehicular perspective dashcam, and videos are sourced from two primary origins. Similar to previous works (47) 22 23 24 (5), we collected videos of pedestrian and vehicle collisions from online platforms such as YouTube, using 'pedestrian vehicle collision' as keyword. In addition, a small number of videos are derived from existing open-source traffic datasets (47 23), which were primarily developed for tasks related to driver attention and the prediction of sudden accidents. From these datasets, we selectively extracted videos that depicted incidents of pedestrian and vehicle collisions. Furthermore, a multi-step filtering process was instituted to ascertain the high quality of the data. Firstly, videos of pedestrian and vehicle collisions within traffic scenarios were selected based on the recorded times and locations. Secondly, we opted for videos shot from first-person perspective, specifically from the viewpoint of dashboard camera. Lastly, a resolution analysis was conducted on the videos, those with resolutions too low for human discermeent were discarded to ensure the clarity and visibility of the content. All of the collected videos were reduced to individual accident footage, recording a complete pedestrian and vehicle collision, resulting in 209 dedestrian-vehccee accident videos, totaling 42,511 frames of images and about 19,533 pre-collision poses. The statistical results associated with video clips and image frames shown in Figure a) and (T.
244 (0.95)
108G (2.04)
(1280 x 7201
337
Colliien
1033
11584
2964
6498
15%
9734
13%
25.4
(1246 7201
(600 x 480)
Bamboe Sooera
(a) Video duration
(b) Frame resolution
(c) Pose class count
Figure A1: PVCP dataset statistics.
A.2 Data Annotations
In scenarios involving collisions, the pedestrian, the vehicles, and the interconnected data between the two are the pivotal components that must be attend. For the application of pedestrian-vehick collision accident reconstruction and vehicle active protection in traffic collision scenarios, PVCP provides rich pedestrian pose representation, including pedestrian clipping images, pedestrian Bounding Box, track id, 2D and 3D keypoints and SMPL mesh label.
A.2.1 Pose Annotations
SMPL Annotation Tool. We used CnCO-annntation (71) as tool for 2D pose annotations and specifically designed an annotation tool 25) for SMPL mesh which was developed based on (72). By importing the cropped pedestrian background and the initial results estimated by SPIN (11), we ensured that the mesh aligned as closely as possible with the pedestrian's background outline the image. The final exported mesh model was used as ground truth labels. The annotation interface and
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Before annotation
After annotation.
Figure A2: Diagram of the SMPL Annotation Tool.
By adjusting the weight of the image background and the mesh foreground, is clearly displayed to facilitate annotation.
the comparison before and after annotation are shown in the Figure A2
A.2.2 Vehicle Annotations
Distance between the pedestrian and the vehicle, as well as the vehicle's ' speed, are crucial factors in collision accidents. In real traffic scenarios, these parameters can often be acquired through the sensors equipped on the vehicle. However, in our collected accident videos, obtaining the distance and speed through sensors is feasible (as these data are not provided). Therefore, we refer to RootNet (73) to obtain the distance between the pedestrian and the vehicle as our dataset's 's distance annotation. Similarly, vehicles are often fitted with additional sensors get accurate speed, but for our crash video dataset it's not perfect and the speed information is missing. Therefore, we use the estimated petesttrian-ehhice distance and video frame rate to obtain an approximate speed label.
A.2.3 Other Annotations
Beyond the aforementioned annotations, we have also annotated the environment of the collision, the cause of the accident, and whether collision ultimately occurred. This additional annotation information serves supplement to our dataset, facilitating its use in related tasks.
A.2.4 Visualization Comparison
Compared to existing pose datasets, PVCP demonstrates significant differences and advantages in terms of scene specificity, action space variation, and temporal continuity. As shown in the Figure A3 we visualize a comparison the differences between PVCP and other pose datasets.
MSCOCO Dataset (20) contains annotations for object detection, panoptic segmentation, and keypoint detection. The images are collected from websites including Google, Bing. and Flickr. The annotations are performed by workers on Amazon's Mechanical Turk (AMT). The dataset contains over 200K images and 250K person instances (35). Although the size of COCO datasets for 2D HPE is large enough for normal pose estimation (e.g., standing, walking, running), these datasets have limited training data for unusual poses, such as falling. The data imbalance may cause model bias, resulting in poor performance on those poses (74). Furthermore, the COCO dataset lacks continuous video frames for pose estimation, which presents challenge for studying sequential pose variations related to temporal features.
Human3.6WV Dataset (16) is the most widely used multi-view single person 3D human pose bench- mark. The dataset is captured in 4mx3m indoor space using 4 RGB camera, time-of-flight sensor, and 10 motion cameras. contains 3.6 million 3D human poses and the corresponding videos (50 FPS) in 15 scenarios, such as discussion, sitting on a chair, taking a photo, etc. Especially. both 3D positions and angles of keypoints are available (35). However, these poses belong to everyday activities and differ in spatial and temporal characteristics from specific poses such as pre-collision poses. Moreover, the dataset, captured in indoor environment, lacks dynamic or static scene backgrounds, which important factors that can affect the robustness of pose estimation algorithms.
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Our PVC
H3MM MSCOCO
PW3D
PedX
Figure A3: Visualization comparison of PVCP with other pose datasets.
Image background is also one of the key factors influencing the performance pose estimation methods (56).
PW3D Dataset 21 is a single-view multi-person in-the-wild 3D human pose dataset that contains 60 video sequences (24 train, 24 test, and 12 validation) of rich activities, such as climbing, golfing, relaxing on the beach, etc. The videos are captured various scenes, such forest, street, playground, shopping mall, etc. They leverage IMU to obtain accurate 3D pose despite the complexity of scenes. Especially, PW3D contains abundant 3D annotations, including 2D/3D pose annotations, 3D body scanning, and SMPL parameters. In some crowded scenes (e.g. on the street), PW3D only provides the label of the target person, ignoring the pedestrians passing by. Generally, the entire dataset is used for evaluation, without any fine-tuning (35). Similar to PW3D, we also annotate pedestrian 2D and 3D keypoints as well as SMPL parameters. However, due scene constraints, this method of data collection is difficuul to replicate in collision scenarios.
PedX Dataset (14) intersections. It includes over 5,000 stereo image pairs and 2,500 frames of 3D LiDAR data, all calibrated and time-synehe menne-syccrooizee.. Collected three four-way intersections, the dataset captures more than 14,000 pedestrian instances from a driver's perspective using roof- mounted stereo cameras. Each pedestrian annotated with 2D and 3D labels, including 18 keypoints, and unique tracking IDs across frames. Using SMPL parameterization, PedX provides accurate 3D models of pedestrian pose, shape, and global position based on stereo images and LiDAR data. PedX a pedestrian pose dataset in traffic scene similar to our PVCP. However, it maintains a fixed viewpoint at intersections, and the pedestrian poses it captures are primarily limited to normal walking postures.
multi-sensor dataset focused on pedestrian-vehick interactions urban
PVCP Dataset focuses on dashcam perspective, capturing pedestrians' emergency behaviors during vehicle movement, such as sudden road crossings or running into the street, within dynamic traflic environments and changing fields of view. It highlights rapid, unpredictable motions like accelerating, swerving, or jumping to avoid vehicles. PVCP categorizes these pre-collision poses into four types, and the PPSENet model leverages pose category loss to learn their spatial distinctions. Pedestrian pre-collision poses are closely linked to the vehicle's time series, occurring in rapid succession as the vehicle approaches, emphasizing the importance of time sensitivity and continuity in algorithms. PVCP provides continuous pre-collision emergency poses of pedestrians, this allows for timely pose predictions and warnings by capturing pose variations from continuous context.
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Joint name
J Joint name pelvis
10
neck
belly
8
right, hip
head
14
11
right, knee
right, ankie
right shoulder
16
left, hip
left shoulder
11
47
12
left, knee
right, hip
12
13
left, unkle
spine
right, elbow
8 nerk
left, olbow
9
nose
nihht, knee
5
10 bead
knoe
10
11 left. shoulder
11 rightt wnst
12 left, elbow
13 left, wrist
left wrist
14
14 right shoulder
night ankle
15 right, elbow
left, ankle
16 right, wrist
(a) 15 keypoints JHMDB
(a) keypoints of Human3.66 6M
Figure A4: Pedestrian pose keypoints representation of PVCP dataset.
B Network Architecture
B.1 Image to Pose
Estimating 2D human pose from images is a basic and mature task, and many works have achieved very effective results in different datasets. We take image frame and corresponding Bbor as imput, simply use ResNet50 (3) as the backbone of feature extraction, and use transposed convolution and a heat map regression head H as the pose estimation network, which also the classic paradigm of 2D pose estimation (8. The resulting 2D pose P2D E 1g will be used as input for the next stage. The testset accuracy of ITP is shown the Table AT The accuracy of Ankle significantly lower because the lower limbs of the pedestrian are heavily shielded during the collision.
P2D (LLDeccomuBackbonee Bbox)))
(AI)
Table Al: 2D detected result of ITP.
Joint
Head
Shoulder
Elbow
Wrist
Hip
Knee
Ankle
Mean
PCK(%)
94.82
93.62
89.61
85.69
93.05
80.05
1.93
78.15
B.2 Pose Mesh
We refer to the JHMDB (60) dataset for annotating human 2D pose keypoints (15 keypoints) and participate the first stage of ITP training. For PTM training. we use the Human3.66 3M6 (16) pose representation (17 keypoints). As shown in Figure A4 there differences in joint positions and numbering between the two. We aligned the common joint positions and calculated the additional joints for Human3.6M:
Jpctvis--H33M (Jieft hippf-hhpprjjmaaa + Jright hiproshomdh) X 0.5
(A2)
J Jspine-H338. (Jpelvis- H33M Jneckr-jhmdd)) x 0.5
(A3)
J Jnoser H36M (Jneck-vjhmdb
bjhmmb + Jhead-)jhmdt) x 0.5
(A4)
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Table A2: Component of system. Top use detected 2D pose sequences. Bottom use GT 2D pose
sequences.
Inpat
Pretrain
Iter
Cluss Loss
Pose cluss
MPVE
PAMPVE
MPJPE 14j
PAMPIPE 14j
MPJPE 17j
PAMPJPE 17J
253.50
Run
253. 16
149.99
219.06
124.01
200.19
115.27
Avoid
286.85
159.69
246.94
124.86
222.02
114.96
Collision
222.47
127.37
120.47
282.50
243.59
132.43
222.70
123.33
Normal
284.39
152.77
239.39
120.68
218.69
112.57
Run
233.78
134.53
200.96
111.19
182.08
102,69
246.02
145.64
207.84
185.50
101 94
Collision
191.91
114.36
176.79
93.02
155.36
$6.44
All
266.20
146.88
225.38
116.99
204.98
108.63
Nomual
275.49
233.23
119.51
111.19
Run
221.62
129.31
192.70
108.35
174.61
100.04
Avoid
247.18
142.64
209.39
110.58
186.96
100.67
259.05
143.82
220.39 39
115.44
200.16
107.00
Nommal
292.79
1493.02
230.49
11747
200.99
109.04
Avoid
251.60
143.52
212.75
109.75
190.00
100.09
Collision
217.68
134.95
201.15
113.10
174.57
105 944
Nommal
132.
Run
129.49
89.31
109.93
70.91
100.19
65,70
Avoid
127.04
85.36
108. 30
65.35
96.74
60.44
Collision
135.89
89.71
127.11
70.86
112.50
64.94
Nomual
156.21
101.34
17
78.38
TT9.00
75:13
Run
128.03
88.85
109.57
70.81
100.04
65.66
Collision
144.93
91.80
132.99
70.99 99
119.26
64.53
2D
All
145.75
96.69
123.16
75.13
111.90
69 69.89
Run
125.01
87.69
106 31
69:42
96.96
64.57
Avoid
117.76
79.36
100.16
61.37
89.80
56. $6.92
Collision
126:20
141.28
92.78
120.16
72.43
108.90
67.58
Normal
148.58
102.71
125.34
79.05
113.42
73.21
Run
126.75
87.65
64.52
Avoid
126.82
86.34
106.94
95.78
60.78
Collision
144.13
97.48
133.35
76.46
117.56
70.12
140.43
96.43
118.80
75.13
107.47
69.56
B.3 Loss Function
The loss function ITP, defined as the Mean Squared Error (MSE), is applied for comparing the predicted heatmaps Å and the ground truth heatmaps H. Following (12), the PTM loss function utilizes SMPL loss and motion loss. Furthermore, The pedestrian pose of the collision sequence has obvious categories, namely normal pose, running pose, avoiding pose and collision pose as described in 3.2 So introduce pose class loss. The final loss function is as follows:
LClase AooCross Entropy (Ć, C?)
(A5)
LPTM = LSMPL LMosio + LClase
(A6)
where Leross Entropy (Ò, C) epresents the cross entropy loss between the predicted pose class and the GT pose class. Ao are the constants of the balance weight loss.
C Experimental and Results
C.1 Evaluation Metric
We evaluated the estimation of 3D human pose and shape using the following metrics, which briefly described as follows.
MPJPE(mm, J). Mean Per Joint Position Error measures the average Euclidean distance between predicted 3D pose and ground truth after root (pelvis joint) matching, which comprehensively evaluates the predicted poses and shapes, including the global rotations.
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Table A3: Comparison of 2D GT imput in different iterations number.
Iter
Pose class
MPVE
PAMPYE
MPJPE 14j
PAMPJPE 14j
MPJPE 17j
PAMPJPE 17j
Normal
150.11
103.04
126.58
79.10
114.60
73,30
Run
128.58
88.81
109.33
70.64
99.28
65.39
2
Avoid
127.58
86.26
107.62
66.13
96.52
60.81
Collision
143.76
95.97
134.13
75.29
117.82
69.08
All
141.95
97.43
120.04
75.45
108.63
69.85
Normal
148.58
102.71
125.34
79.05
113.42
73.21
126.73
87.65
107.87
69.62
97.97
64.52
3
Avoid
126.82
86.34
106.94
66.11
95.78
60.78
Collision
144.13
97.48
133.35
76.46
117.56
70.12
140.43
96.43
75.13
107.47
69.56
Normal
148.11
102.79
124.99
79.21
113.11
73.38
Run
126.12
87.22
107.46
69.25
97.61
64.23
4
Avoid
126.66
86.74
106.80
66.40
95.58
61.06
Collision
144.69
98.67
133.11
77.44
117.68
71.05
All
139.96
96.92
118.46
75.19
107.16
69.62
Normal
148.17
103.04
125.08
79.51
113.24
73.66
Run
126.08
87.11
107.50
69.14
97.67
64.17
5
Avoid
126.83
87.28
106.94
66.82
95.69
61.48
Collision
145.48
99.73
133.25
78.33
118.12
71.92
All
140.01
97.10
118.54
75,40
107.27
69.83
Normal
148.59
103.39
125,45
79.88
113.67
74.02
126.40
87.19
107.82
69.18
98.03
64.26
Avoid
127.27
87.93
107.29
67.34
96.04
62.00
Collision
146.56
100.73
133.70
79.21
118.88
72.80
140.41
97.42
118.89
75.70
107.68
70.14
Table A4: Comparison of results on PVCP testset after training other datasets.
Input
Train
Test
Pose class
MPVE
PAMPVE
MPJPE 14j
PAMPJPPE 14j
MPJPE,
PAMPJPE 17]
Normal
191.45
118.21
167.35 38
92.63
153.22
87.62
119.20
COCO
PVCP
Aveid
160.62
99.18
136.47
78.21
124.86
72.87
90.39
All
185.57
116.28
161.28
91.24
147.61
85.65
206.31
134.79
159
98.70
Run
181.16
129.40
154.96
104.39
139.40
94.35
Avoid
137
Collision
154.24
90.50
141.18
77.98
127.11
71.19
95.17
Normal
148.58
102.71
125.34
79.05
113,42
73,21
126.73
87.65
107.87
69.62
97.97
64.52
PVCP
PVCP
Avoid
126.82
86.34
106.94
66.11
95.78
60.78
(finctuning)
Collision
144.13
97.48
133.35
76.46
117.56
70.12
All
140.43
96.43
118.80
75.13
107.47
69,56
PA-MP.JPE(mm. J). Procrustes-Alignis Mean Per Joint Position Error denotes MPJPE after rigid aligmment of the predicted 3D pose and ground truth, which eliminates the discrepancies scale and global rotation.
MPVE(mm, 1). Mean Per-vertex Error, initially aligns similarly MPJPE. is characterized by the average point-to-point Euclidean distance between predicted mesh vertices and corresponding ground truth vertices.
PA-MPVE(mmm.)) J). Procrustes-Alignen Mean Per-vertex Error represents MPVE after applying Procrustes alignment between predicted mesh vertices and ground truth vertices, similar how PA-MPJPE is calculated.
Further, test the errors of 14 keypoints (X 14j) shared 2D and 3D pose representations (21) and 17 keypoints (X ___1)) represented only by 3D pose representations (16) respectively. For 2d pose estimation,
PCK as metrics.
PCK(%, 1). Percentage of Correct Keypoints measures the accuracy of joint positioning in the body. If the candidate body joint falls within the threshold pixel of the GT joint, it is considered correct.
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C.2 Ablation Study
Component of system. the stage of lifting 2D poses to 3D meshes, we incorporated an iterative decoder. Instead of predicting the output in a single step, we iteratively refined the results to gradually approach the optimal solution. Additionally, we included a pose classification detection head and used a class loss as supervision. We compared the impact of different components on the network's s performance, reloading and training the model each time, and set the optimal number of iterations to 3. Under the combined effect of these two modules, the pose sequences from both types of input achieved minimal error. As shown in Table A2 we provide a detailed comparison of the errors for different pre-collision pose class. The iterative decoder significantly improved the accuracy across all pose categories, and the introduction of the pose category loss function further enhanced the accuracy of pre-collision poses.
Number of iterations. As shown in the Table A3 we combined all modules verify the accuracy of different iterations of 2D GT input to select the optimal number of iterations. We verify the test results of increasing the number of iterations from 2 to in turn, and it can seen that the test results of different iterations are excellent. When iter=3 or 4, the average error of each metric is the lowest value. We chose iter=3 as the final number of iterations for experimental comparison.
Without Fine- Fine-Tuning. As shown in the Table A4 we trained on the COCO and PW3D dataset respectively and validated on the PVCP testset. Because these datasets do have pose category annotations, we set them uniformly to a single category. The results show that training only on another large human pose dataset without fine-tuning for PVCP does not perform well on PVCP this particular pose dataset. The conclusion that can be drawn is that training solely on other large human pose datasets, such as COCO and PW3D, without fine-tuning for PVCP, does not yield good performance on the PVCP dataset. This indicates that there are significant differences between datasets, particularly when dealing with specific scenarios and unique poses, and that fine-tuning and optimization tailored to the PVCP dataset are necessary to improve model performance.
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Justification: Our work provides data and algorithmic support for pedestrian pre-collision pose estimation and active safety protection for pedestrians
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Reviews:
[{"review_id": "eIF2DzlBaZ", "reviewer": "Reviewer_SmGM", "summary": "This work focuses on the pre-collision posture of pedestrians in real traffic scenarios. The authors semi-automatically constructed the first pedestrian-vehicle collision pose dataset PVCP by collecting pedestrian-vehicle collision poses from the perspective of dashcams, which includes more than 40,000 accident frames and more than 20,000 pedestrian pre-collision pose annotations. Furthermore, this work constructs a pedestrian pre-collision pose estimation method(PPSENet) to estimate the collision pose and morphological sequence of pedestrians from pedestrian-vehicle accident videos. PPSENet first estimates the 2D pose from the image (image to pose, ITP), and then promotes the 2D pose to a 3D mesh (pose to mesh, PTM). Experiments show the relative advantages of PPSENet.", "questions": "I hope the author will emphasize the breakthrough and innovation brought by the proposed method PPSET. I think the starting point of the dataset is very good, and the content of the dataset is also very good. But I think the corresponding method should be equally good to meet the standards of NeurIPS.", "limitations": "See Weakness.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. I consider the dataset collected in this work is meaningful. It can provide a well guide for many autonomous driving methods. Although the pose before the collision may not even last for a second, the human pose data at this moment is indeed worth studying.\n2. The author designed an SMPL annotation tool to align the initial results with the image contour. The PVCP dataset finally obtained about 40,000 accident images and more than 20,000 2D and 3D annotations of pedestrian emergency poses.", "weaknesses": "1. I consider  the PPSET method is the weak point of this work. From a methodological perspective, except for the robust 2D pose estimation method in the first stage, the second stage is almost similar to the backbone network of MotionBERT. There is almost no innovation. From an experimental perspective, the results of 2D pose sequences in Table 2 are really poor. Although PVCP is a brand new dataset for pre-collision human poses, the network input is sequences, and the overall 2D joint index, MPJPE is greater than 100. In the results of Table 5, Pose2Mesh performs better than MotionBERT without being trained on PVCP. I highly doubt this experimental result. However, PARE, a one-stage method that has not been retrained, can outperform PPSET in the first four indicators. We know that PARE is from the work in 2021. From this result, I think PPSET is completely at a disadvantage.\n2. Although PVCP has collected a relatively large number of pedestrian-vehicle collision pose datasets, it is not the first one. [1] As early as 2020, there was a description of the relevant dataset, and the video in it is the same as PVCP. Although I saw the relevant reference, it was glossed over. The statement in line 110 of Related Work should be modified.\n3. Although I think the annotation application of SMPL is an innovation, the author also cleverly hid the relevant github content. However, the relevant GUI interface had similar content as early as 2020 [2].\nTypo:\n1. The two time spans in Year in Table 1 should be based on \"2017\" instead of \"1017\"\n2. The use of \"impractical\" in line 46 is easy to cause ambiguity. Because PVCP is doing such content.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work and especially the significance of the PVCP dataset. \n\nAnd  thank you very much for your review. We will address your comments point by point.\n\n------\n\n**Reply1.**  Our PPSENet employs a two-stage strategy. In the first stage (ITP), we train using the PVCP dataset for 2D pose estimation. In the second stage, we select MotionBert as the baseline for our second phase (PTM) and make improvements based on it. We chose MotionBERT as baseline for the following reasons:\n\n- **SOTA for Temporal Pose Estimation**: At the start of our work, this was the SOTA method for temporal human pose estimation and could be easily transferred to downstream tasks.\n- **Pose Prior for Large-Scale Data**: Pedestrian-vehicle collision events are rare, making our PVCP dataset relatively small compared to other large datasets such as Human3.6M. MotionBERT’s pre-trained model on large-scale datasets provides a good pose prior.\n\nHowever, we innovatively added an auxiliary loss for collision pose classification, tailored to the pre-collision poses in our dataset, and employed a simple iterative regression approach. Under identical conditions, our PPSENet significantly outperformed MotionBERT on the PVCP dataset (see paper Table 2), demonstrating the effectiveness of our improvements. We have submitted the source code  to the AC with the paper version and make them public subsequently.\n\nIndeed, while our PPSENet outperforms the original MotionBERT on PVCP dataset, the quantitative results are not exceptionally high. We believe the reasons are as follows:\n\n   -  **Annotation Challenges**: 3D pre-collision pose annotations in traffic scenes cannot be obtained through motion capture (Mocap) but are aligned using  our annotation tools with the SMPL foreground and image background contours.\n   -  **Complexity of Pre-Collision Poses**: Pre-collision poses differ from daily human poses. As a specific type of pose, the inherent complexity of the pedestrian structure makes it difficult to achieve the same quantitative results as other pose datasets.\n   -  **Dataset Size**: Although our PVCP dataset contains over 20K annotations of pedestrian pre-collision poses, its size is still relatively small compared to the training results of large-scale datasets such as MSCOCO (＞1000K) and Human3.6M (＞500K) .\n\nThese factors contribute to the relatively poor quantitative performance of our results. We will strive to improve our dataset and algorithm, hoping to achieve better results in future research.\n\nOur PPSENet is a two-stage method. In Table 5, we compare it with both single-stage and two-stage algorithms. Indeed, the single-stage PARE achieves relatively good results. In this section, we tested the PVCP’s testset. Before procrustes-aligned, MPJPE and MPVE achieved better results, but after procrustes-aligned, our PPSENet outperformed PARE in PA-MPJPE and PA-MPVE. We hypothesize that due to data collection constraints, we could not obtain camera parameters and the world position of the pedestrian. When considering pedestrian orientation and position, we only used annotation tools for alignment. Ignoring pedestrian orientation, position, and camera parameters, our method outperforms PARE in human pose metrics after  procrustes-aligned.\n\n------\n\n**Reply2.**  Thank you for your correction.   Regarding our description of \"the first relevant dataset,\" it was indeed inappropriate. We will replace \"first\" with \"a\" to make the expression more precise. As follows:\n\n> Using a semi-automatic method,  we collected dashcam videos of collisions to create ~~the first~~  (a)  pedestrian-vehicle collision pose dataset,…\n\n------\n\n**Reply3.**  We apologize for hiding the complete GitHub link of the annotation tool due to the requirement of anonymity. We have submitted the complete and anonymous code for this submission to AC for review. \n\nAs mentioned in the supplemental material, our SMPL Annotation Tool is not a completely new development. Instead, we improved upon the tool mentioned in [Reference 66 of paper], which was originally designed for merely viewing various SMPL parameters. We have added many interactive modules to make it capable of annotating SMPL, including functionalities such as importing background images, automatically saving annotations, adjusting the weights of foreground and background to meet our annotation requirements, and annotating our dataset based on these functionalities. We hope that this annotation tool will be helpful to the work related to SMPL datasets.\n\n------\n\n**Reply4.**  I apologize for making such a basic mistake. Thank you for your careful review. We will correct the year to the correct one in the final revision.\n\n------\n\n**Reply5.**  Thanks for your correction, our expression is really easy to cause confusion, so the following changes have been made to line 46:\n\n> Original: Dashcams or public surveillance devices are the only sources of data, ~~and constructing a large-scale dataset of pedestrian pre-collision poses for network training is similarly impractical.~~\n\n> Revised: Dashcams or public surveillance devices are the only sources of data , **further constrains the approaches to dataset creation, thereby enhancing the difficulty and complexity of producing such datasets.**\n\n------\n\nThanks again for your comments.  We will address all suggestions in the final revision, and some additional notes will also be extended to the newly submitted supplemental material.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "okmqN5i3Im", "reviewer": "Reviewer_9csR", "summary": "This paper collects a new dataset that includes pedestrian-vehicle collision poses from the dashcam perspective and presents a two-stage framework for estimating human pose and shape parameters. The dataset has the potential to benefit the study of pedestrian injuries. Given the challenge of obtaining ground truth pose and shape parameters in the wild, the authors employ the two-stage framework to generate initial results. These are then refined through manual adjustment using their annotation tool.", "questions": "1. How does the author ensure the motion smoothness of results during the manual annotation refinement stage?\n2. How many annotators are assigned to each video clip?\n3. How annotators are recruited and trained to ensure accuracy?", "limitations": "The dataset is relatively small in size and no camera parameters are provided. Accurate vehicle speed and the global position of pedestrians could also be helpful for collision damage analysis.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper makes a significant contribution by collecting real-world pedestrian pose and shape parameters before collisions. The datasets include useful modalities such as bounding boxes and tracking information. Since it is difficult to obtain human poses in uncontrolled environments, the authors collect online dashcam videos and introduce a straightforward framework to estimate initial results. This framework iteratively regresses the pose and shape parameters.", "weaknesses": "Since the dataset uses videos collected from the Internet, it is difficult to obtain the ground truth pose and shape parameters. The accuracy of a dataset is important. The paper could elaborate more on the procedures to ensure accuracy such as how many annotators are recruited, how they are trained before the annotation, whether there is a post-processing stage, etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work in pedestrian injury research. \n\nThank you very much for your careful review, and we will reply to your opinions on Questions and Limitations respectively.\n\n------\n\n##### **Questions Reply:**\n\n**Reply1.**  Unlike regular and periodic postures, pre-collision postures exhibit strong specificity and abrupt changes. After manual annotation, we performed simple smoothing operations (such as the moving average method), but for pre-collision postures with particularly large variations, the smoothed postures significantly differed from the GT (ground truth) postures. Therefore, to ensure the accuracy of joint positions, we retained the manually adjusted annotations. **In a separately submitted PDF, we show an example of a 2D posture sequence with smoothing operations to support our claim (see Figure R2.)**\n\n------\n\n**Reply2.**  In order to obtain relatively accurate pose annotation data while controlling the cost of dataset production, after clipping the videos into independent segments, we arrange two annotators for each video and conduct effective annotation through the following three steps:\n\n   - 1） Firstly, we assign one annotator to perform the initial annotation for each video, ensuring that the SMPL human pose aligns as closely as possible with the task contour of the background image;\n\n   - 2） After the initial annotation is completed, the second annotator reviews the annotations, checking for omissions, errors, or incomplete alignments;\n\n   - 3） Finally, any discrepant labels are sent back to the first annotator for re-annotation to ensure correctness.\n\nThis process is repeated until there are no more discrepancies in all annotations. In this way, we obtain visually consistent SMPL pose annotations that closely match the images. Subsequently, the annotated pose data is sent for subsequent post-processing.\n\n------\n\n**Reply3.**  Our annotation tool is designed for ease of use, requiring minimal to no training. Simply adjust the sliders for pose and shape parameters of the corresponding  joints based on the interface prompts, aligning the SMPL pose with the background pedestrian silhouette. The annotated files are automatically saved, and we will subsequently conduct unified data post-processing. To aid in comprehension, **we have included an anonymous link in this Rebuttal submission to the AC, showcasing a simple annotation process video.**\n\n------\n\n**Limitations Reply:**\n\nIndeed, our dataset lacks camera parameters due to the fact that real-world vehicle-pedestrian collisions are accidental events that are difficult to systematically record. Most of our data is sourced from online platforms, which is one of the challenges and limitations of related datasets. Additionally, accurate vehicle speed and the global position of pedestrians could be valuable for collision damage analysis. **In our appendix (A2.2), we have proposed a simple solution to annotate this information:**\n\n> A.2.2 Vehicle Annotations\n>\n> Distance between the pedestrian and the vehicle, as well as the vehicle's speed, are crucial factors in collision accidents. In real traffic scenarios, these parameters can often be acquired through the sensors equipped on the vehicle. However, in our collected accident videos, obtaining the distance and speed through sensors is not feasible (as these data are not provided). Therefore, we refer to RootNet [67] to obtain the distance between the pedestrian and the vehicle as our dataset's distance annotation. Similarly, vehicles are often fitted with additional sensors to get accurate speed, but for our crash video dataset it's not perfect and the speed information is missing. Therefore, we use the estimated pedestrian-vehicle distance and video frame rate to obtain an approximate speed label.\n\n------\n\nThanks again for your comments.  We will address all suggestions in the final revision, and some additional notes will also be extended to the newly submitted supplemental material.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TIWM8nW8qT", "reviewer": "Reviewer_3M7p", "summary": "The paper conducts the first Pedestrian-Vehicle Collision Pose dataset(PVCP) including the pedestrian-vehicle collision pose from the dashcam perspective and pose annotations. Moreover, the paper presents the method(PPSENet) to estimate the collision pose and shape based on PVCP.", "questions": "Will the SMPL annotation tool be made publicly available?", "limitations": "The author addresses the limitations in the apendix.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The first Pedestrian-Vehicle Collision Pose dataset, annotated by algorithm and manual method.\n2. The paper proposes the framework(PPSENet) to estimate the 2D pose and lift it to the 3D pose. The method achieves SOTA in PVCP", "weaknesses": "Because the dataset annotation largely depends on manual methods. The paper may need more discussions or a more convincing evaluation, such as more visualization of the dataset or a video.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work, especially the PVCP dataset and annotation tools. \n\nThank you very much for your careful review. \n\n------\n\nIndeed, our annotation tool has already been made accessible to the public, but due to anonymity requirements, we have withheld the full GitHub link. We will include the complete link in the final version of our paper, so that more researchers can utilize and improve upon our work. \n\nIn this rebuttal, **we have submitted an anonymous link to the AC, which provides access to all source code, datasets, annotation tools, and additional visualization videos (including dataset videos and experimental visualization results).** We will also make the papers publicly available once they are fortunate enough to be accepted for publication.\n\nThe PVCP dataset is a novel pedestrian pre-collision pose dataset, and we believe it holds significant value for studying pedestrian behavior and enhancing pedestrian safety. Given the constraints of traffic scenarios and video sources, we had to rely on manual annotation for human pose data. Fortunately, we were able to leverage the support of many previous excellent open-source efforts to accomplish this task. We will continue to refine and enhance the PVCP dataset and our algorithms in order to achieve even greater progress.\n\n------\n\nThanks again for your comments.  We will address all suggestions in the final revision, and some additional notes will also be extended to the newly submitted supplemental material.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JLVNEklPh1", "reviewer": "Reviewer_j3Vv", "summary": "This paper proposed a dataset (PVCP) followed by a solution (PPSENeT), which is the first one that focuses on the 3D pre-collision pose of pedestrians. The dataset is collected by dashcam that contains various poses with both static and dynamic backgrounds. Various types of annotations are provided, including 2D, 3D, and human mesh, leading to diverse evaluation protocols. Following the dataset, the method (PPSENet) is a top-down method, that first crop the human subjects with a bounding box and then predict the 2D poses. The 2D pose will be lifted to a 3D mesh with the help of iterative regression and a novel pose class loss, following MotionBERT backbone. All combined, the method achieves SOTA performance on the proposed PVCP dataset.", "questions": "1. What is the processing time (inference time) of the proposed method? Is it a real-time method?\n\n2. What is the performance of the method only trained on COCO or another large human pose dataset without finetuning on the PVCP?\n\nFor the other two questions, please see the weaknesses. These questions are the key to changing the rating.", "limitations": "No negative societal impact is found.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The proposed dataset contains various types of annotations, including 2D, 3D, and human mesh. This contains abundant ways of evaluation for every method.\n\n2. The topic is in general interesting. It is a very good intention to expand the scope of the daily human pose to the pose in a specific setting. Especially, in such a setting related to the safety of daily life.\n\n3. The paper is generally good-written and easy to follow.", "weaknesses": "The major problem addressed by the paper is somewhat trivial to me. Typically, there are two logical holes that the author fails to fulfill before they propose this dataset and solutions.\n\n(1). Are the pre-collision poses really so different from normal or daily poses? This is intuitively not correct in my perspective. The pre-collision pose is a subset of the daily pose. In this spirit, the model trained on a large human pose set (such as COCO), should work decently on the pre-collision pose. The pose such as running or avoiding, are covered in COCO (as COCO also contains various). COCO has even more diverse scenes (indoor and outdoor), while PVCP presumably only contains outdoor scenes (as it is for pre-collision poses). In this case, authors should give solid evidence to support their claim. Moreover, in the author's Fig1(c), the proposed dataset PVCP, seems even more general than COCO, which is contradictory to my intuition. \n\n(2). Are the pre-collision poses really an important factor in terms of avoiding collision? There are many factors that could influence the rate of avoiding collision, such as the speed of vehicles or the light conditions as the authors also mentioned in the appendix. Are there any experimental results to support having the pre-collision poses will significantly reduce the rate of accidents?\n\nIf the above two questions are not answered properly, then it seems this problem is not really a significant problem to be investigated, which will reduce the necessity of the proposed dataset and method.\n\nThe proposed method, on the other hand, follows a top-down paradigm, which is known as not compatible with real-time applications, not to mention that architecture is used to produce 3D mesh. However, the pre-collision pose estimation definitely requires real-time processing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your positive feedback and valuable comments on our dataset and paper writing.\n\nThank you very much for your careful review, we will reply to your opinions on Questions and Limitations respectively.\n\n------\n\n**Questions Reply:**\n\n**Reply1.**   We fully understand your concern about differences in data sets. Indeed, in a broad sense, the pre-collision pose is a subset of everyday poses, since similar poses can always be found in everyday life. However, PVCP  is significantly different from other pose datasets (such as COCO) in the aspects of **scene specificity**, **action space difference** and **time series continuity**. Here's a breakdown of these differences:\n\n**Scene specificity**: PVCP focuses on the dashcam view, capturing pedestrians' emergency behaviors during vehicle movement, such as sudden road crossings or running into the street, against a dynamic traffic background and changing observation field. This is crucial for road safety. COCO, while including some road scenes, covers a wide range of everyday human movements that aren't specifically related to potential vehicle collisions.\n\n**Action space difference**: Pedestrian pre-collisions in the PVCP's dashcam involve rapid, sudden motions like accelerating, swerving, or jumping to avoid a vehicle. These actions are highly dynamic and uncertain in time and space, making detection accuracy crucial. The PVCP dataset categorizes these pre-collision poses into four types, and PPSENet uses pose category loss to learn their spatial differences. In contrast, COCO's common poses cover broader, more static motions. While some include movement, they aren't specific to emergency vehicle collision situations, making their spatial distribution and dynamics more stable and predictable.\n\n**Time series continuity**: In traffic recordings, a pedestrian's pre-collision poses are closely tied to the vehicle's time series. These poses occur in quick succession as the vehicle approaches and is about to collide, showing strong time sensitivity and continuity. Algorithms must capture and analyze these pose variations from a continuous context to provide timely pre-pose estimates and warnings. In contrast, the COCO dataset's poses do not emphasize time series continuity. Static poses in single images cannot reflect time-sensitive changes in human movements, especially those related to emergency vehicle collisions, making COCO poses relatively loose and independent in time series.\n\n**The attached PDF compares the visual differences between PVCP and other datasets (see Figure R1).**\n\n------\n\n**Reply2.**  Indeed, there are many factors affecting pedestrian collision injury: pre-collision pose, pedestrian position, vehicle speed, vehicle front end shape and so on. Each factor has a lot of work to do to analyze their impact on injury in accidents. Our work focuses on estimating the most uncertain pedestrian precollision posture to help in accident reconstruction and pedestrian injury assessment.\n\nMany studies have demonstrated the impact of pedestrian's initial posture on vehicle-vehicle collision. **Paper [1]** showed  \"Head impact locations on the vehicle are predictable but their severity varies with pre-impact stance by up to 30%”. **Paper[2]** showed higher sensitivities to the pedestrian posture and its relative position with respect to the vehicle than to the  vehicle speed for the chosen design space.  **Paper [3]** shows the pedestrian’s rotation is highly influenced by leg and  arm posture,  which makes predictions of head impact risk in secondary impact difficult. **Paper [4]** investigate the effects of preimpact pedestrian  posture on the loading and the kinematics of the lower extremity when struck laterally by vehicle.  The experiment showed that \"the walking posture increased the injury risk of soft connection tissue about 20-30% and reduced the internal force.” **paper[5]** showed that pedestrian injuries differ in different  gait serials. So do different gaits in the same gait serial. **Paper [6]** states that \"it has been demonstrated that resulting impact kinematics and dynamics from vehicle–cyclist/pedestrian collisions are influenced significantly by  initially specified human posture and motion”.\n\nThis might be briefly described in the \"Related Work\" section,  we will expand on this in the final version.\n\n**References**\n\n> [1] Effects of pre-impact pedestrian position and motion on kinematics and injuries from vehicle and ground contact.\n>\n> [2] Crash reconstruction of pedestrian accidents using optimization techniques.\n>\n> [3] Vehicle related influence of post-car impact pedestrian kinematics on secondary impact.\n>\n> [4] Influence of pre-impact pedestrian posture on lower extremity kinematics in vehicle collisions.\n>\n> [5] Pedestrian gaits observed from actual pedestrian-vehicle collisions.\n>\n> [6] Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation.\n\n------\n\n**Limitations Reply:**\n\n**Reply1.**  Our method is not real-time at present, because **our input is Image and  pre-selected Bbox sequence** of collision pedestrian targets, the purpose of our work is to provide pose data support for the study of vehicle active and passive protection system, so as to facilitate subsequent accident reconstruction, pedestrian injury assessment and vehicle structural design.\n\n**Reply2.**    We trained on the COCO and PW3D dataset respectively and validated on the PVCP testset. Because these datasets do not have pose category annotations, we set them uniformly to a single category. **The results, recorded in the submitted PDF, show that training only on another large human pose dataset without fine-tuning for PVCP does not perform well on PVCP this particular pose dataset (see Table R1) .**\n\n------\n\nThanks again for your comments.  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Author rebuttal:
We appreciate the thorough review and insightful comments from AC and all reviewers. **The reviewer's comprehensive feedback is as follows：**

- **Reviewer SmGM** acknowledged the importance and quality of our PVCP dataset for autonomous driving research and sought further opinions on the innovativeness of PPSENet and related datasets.

- **Reviewer 9csR** affirmed that "This paper makes a significant contribution by collecting real-world pedestrian pose and shape parameters before collisions." And  reviewer  inquired about the annotation process, post-processing, and requested more visualizations of the dataset.
- **Reviewer 3M7p** praised our PVCP dataset and PPSENet algorithm, and inquired about the open-sourcing of the dataset annotation tool.
- **Reviewer j3Vv** found the topic generally interesting, commending the intention to expand the scope of daily human pose to poses in specific settings, particularly those related to daily life safety. Reviewer  remarked that "The paper is generally well-written and easy to follow." Further, reviewer questioned the differences, uniqueness, and necessity of our PVCP dataset compared to others, as well as the implementability of the real-time performance and its performance when trained on other large datasets.

------

**We have thoroughly addressed all the comments and, during this period, accomplished the following tasks:**

1. We have provided detailed responses and revision plans to **each reviewer's comments in a separate Rebuttal** document.

2. We have supplemented the relevant **experiments and visualization results in a separate PDF submission**, including:
   - Visual comparisons of PVCP with other pose datasets (MSCOCO, Human3.6M, PW3D, PedX). `(For all Reviewers)`
   - Smoothing post-processing of the dataset. `(For Reviewer 9csR, Questions Reply: Reply1)`
   - The performance of the method when trained solely on another large human pose dataset (COCO, PW3D) without fine-tuning on PVCP. ` (For Reviewer j3Vv, Limitations Reply: Reply2)`

3. We have **submitted anonymous source code links** for the SMPL annotation tool, PVCP dataset description, and the method's source code to the AC. These links facilitate efficient review of our work by the AC and reviewers. The links include:
   - SMPL Annotation Tools Code` (For Reviewer 3M7p)`
   - Annotation process video` (For Reviewer 9csR, Reply3)`
   - PVCP dataset and PPSENT Readme.md `(For all Reviewers)`

If you have any further questions, please feel free to discuss with us, and we will actively revise and improve our paper. Thank you again for your review.

Meta-review: The submission presents a novel pedestrian-vehicle collision pose dataset (PVCP) and a corresponding pre-collision pose estimation method (PPSENet). While the dataset is a significant contribution, offering valuable data for autonomous driving research, the proposed method lacks substantial innovation and shows limited performance improvements over existing models. Reviewers expressed mixed opinions, with the majority leaning towards borderline acceptance, acknowledging the dataset's importance but criticising the method's originality and results. Given the dataset's potential impact and the authors' thorough rebuttal addressing key concerns, AC is inclined to accept the paper on the condition that the authors publicly release the dataset, models, and annotation tools, and incorporate all reviewer-suggested modifications to improve the work's robustness and transparency.
This decision has been approved by Senior AC.
Decision: Accept (poster)