Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition
Authors: Yiting Dong
Yang Li
Dongcheng Zhao
Guobin Shen
Yi Zeng
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: violence detection
privacy protection
event-based dataset
Dynamic Vision Sensors (DVS)
bullying

Abstract: The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment.To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.

Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition
Yiting Dong 1.3,4 Yang Li2.3,4, Dongcheng Zhao Guobin Shen 134,4 Yi Zeng' 12.33.2.3.47 .23344f
1 School of Future Technology, University of Chinese Academy of Sciences
2 School of Artificial Intelligence, University of Chinese Academy of Sciences Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences CCenter for Long-term Artificial Intelligence
Abstract
The prevalence of violence in daily life poses significant threats individuals' physical and mental well-being, Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deploy- ment. To address the problem, we leverage Dynamic Vision Sensors (DVS) camera to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying108 dataset, en- compassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. will serve as a valu- able resource for training and developing -rottcccio video systems. The Bullying I OK opens new possibilities for innovative approaches these domains.
Introduction
The issue of violence daily life poses a significant threat to individuals' physical and mental well-being. In addition to merely punishing for violent actions, is crucial to deter and prevent their occurrence proactively. Implementing surveillance cameras in public spaces has effectively facilitated the prompt detection of emerging violent behavior. While this strategy has curbed violent incidents [1, 2], the widespread deployment of these cameras stirs up concerns over potential invasions of individuals' privacy, leading to significant apprehensions.
The proliferation of cameras has dramatically enhanced the ease of data collection. Cameras are com- monly employed for indoor and outdoor surveillance, capturing instances of violence emergencies [3, 4, 5J. Nonetheless, this data-gathering method frequently requires obtaining explicit consent from recorded participants for public data collection. Obtaining comprehensive consent from individuals captured on camera poses significant challenges [3, 6]. addition to capturing movement data, personal information related to privacy, such as facial features and attire, is recorded and potentially stored on untrusted third-party servers with high high-performance capabilities, thereby intensifying the potential for privacy breaches. Privacy refers to personal information that individual does not wish
'These authors contributed equally.
1 "ooresponndig author; yi.zeng@@ia iiaanngaaacccc
Project website: https://www. brain-cog networr/daatteee/Bllliingg 10k/
Dataset website: https://igghaae.commaiiiiiee contaattdeevlasse////lllngggggo.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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event frame
event frame
(d)
skeleton
skeleton
event frame
event frame
skelcton
event frume
evvntt framee
(c)
shefeton
sheleton
Figure 1: Visualization of the Bullying 10K dataset. For each example, the right section illustrates the stream of events captured by a Dynamic Visual Sensor (DVS) camera, showcasing the dynamic changes in brightness each pixel. The left section demonstrates the related event frame transformed from the event stream and corresponding human pose keypoints labels. This enables us to observe specific actions and scenes.
to be accessed or understood by others. The disclosure of such information might result a loss of benefits or harm to the individual's interests. Privacy preserving involves measures to ensure that private information contained within data is either eliminated or made extremely difficult to extract. Facial data is one of the most commonly used types of information for identity recognition. It has been widely adopted for applications such as facial payment systems [7] and device unlocking [8], making one of the most concerned about in terms of privacy. Therefore, in this article, the primary focus of privacy protection is on facial information used in critical payment and unlocking scenarios rather than broadly on whether an individual can be identified. At present, most of the commonly employed violence detection datasets primarily utilize RGB images. We aim to devise a strategy that effectively identifies unusual and violent incidents while minimizing the risk of privacy breaches during normal circumstances.
Dynamic Vision Sensors (DVS) cameras [9], which capture pixel brightness variations, provide an innovative alternative to conventional cameras that produce image frames at fixed frequencies. Instead, DVS cameras generate event stream that records each pixel's brightness changes, either enhancement or reduction. As a result, it becomes challenging to visually identify the captured objects. Although some techniques attempt reconstruct images from DVS data [10, 11]. these approaches grapple with issues such as low contrast and blurriness and may even require additional sensor information for assistance [11]. Consequently, extracting detailed user information suitable for recognition systems from DVS cameras becomes a significant challenge, naturally reinforcing privacy persevering measures. At the same time, the high sensitivity of DVS cameras ensures their stable performance under uncontrolled luminary conditions and diverse environmental states [12]. As an event-driven camera, DVS consumes low power when the scene static, reducing energy consumption and mitigating information redundancy compared to traditional cameras. However, although datasets captured using DVS cameras exist [13, 14, 15), most are employed for traditional image classification tasks. Existing action recognition datasets [16, 17] primarily focus generic

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video Table 1: Comparison of different neuromorphic datasets.
Dataset
YYear
#Sensor
#Type
#Object
#Sec Per Example #pose #Event Count
ASLAN-DVS [18]
2019
Davis240c
reproduced
action
No
N-MNIST [14]
2015
ATIS reproduced
digit images
0.3s
300M/4K
N-CALTECHI01 [14]
2015
ATTS
reproduced
images
0.3s
1B/0. IM
DVS-CIFAR10 [13]
2017
Davis128 reproduced
images
1.2s
2B/0.2M
HMDB-DVS [18, 19]
2019
Davis240c reproduced
action
19s
No
3B/0.5M
ES ES-ImageNet [20]
2021
conversion
images
UCF-DVS [18, 21]
2019
Davis240c
reproduced
action
25s
No
12B/0.9M
N-Omniglot [15]
2022
Davis346
reproduced real
char images
DVS-Gesture [17]
2017
Davis128
action
6s
No
500M/0.4M
NCARS [22]
2018
ATTS
real
cars
0.1s
95M/4K
ASL-DVS [23]
2020
Davis240
real
hand
0.1s
2B/21K
PAF [16]
2019
Davis346
real
action
5x
No
DailyAction [24]
2021
Davis346
real
action
5s
No
Bullying10K
2023
Davis346
real
action
2-20s
Yes
12B/1.2M
simple action recognition with limited scale and simplistic labels. Thus, they are insufficient for detecting complex and rapid actions and overlapping individuals, characteristic of violent incidents.
To address these concerns, we leverage the unique characteristics of DVS cameras and propose an event-based dataset called Bullying10K, The dataset aims detect violent incidents in videos while ensuring privacy protection. Instead of relying on conversion algorithms or reproduction methods that be time-saving and resuurce-saving, using DVS cameras. This approach allows us to avoid data biases that may arise from the process of RGB cameras of original datasets. The dataset captures subjects engaging in various actions under different views and lighting conditions. In addition to data privacy persevering, the Bullying IOK dataset stands from other DVS datasets by encompassing more complex and rapid actions and instances where individuals may obscure each other. This inclusion introduces new challenges to event-based neuromorphic datasets.
chose capture real-life scenarios and subjects
In conclusion, the design of the Bullying 10K dataset aims to fulfill the real-time detection require- ments of violent behavior while maximizing the privacy protection of the individuals captured in the footage. This dataset will serve as valuable training data for developing systems, providing new insights and opportunities for future research.
Our contributions are as follows:
1. We propose large-scale DVS bullying recognition dataset: Bullying IOK It contains 10,000 event segments, totaling 12 billion events and 255 GB of data. The actions in the videos are characterized by their complexity, rapidity, and occlusion of individuals.
2. We provide three benchmarks for comparing the performance of different methods: action recognition benchmark, an temporal action localization benchmark, and pose estimation benchmark. For the pose estimation task, we provide the keypoints of human pose.
3. We present the DVS community with trainable dataset to detect violent scenes without compromising privacy. It makes the anticipation and research of violent scenarios possible.
2 Related Work
DVS Dataset Early DVS datasets were typically derived from pre-existing image classification datasets [13, 14]. They captured the brightness differences of pixels caused by camera or image motion using DVS cameras. However, generating meaningful temporal data from static images proved challenging. [17, 16] captured people real scenarios, showcasing different actions through hand movements and providing early event-based classification task benchmarks. However, these datasets were relatively small, and actions displayed were somewhat repetitive. contrast to traditional classification tasks, [15] introduced dataset for few-shot tasks, reconstructing the drawing process of character strokes and creating meaningful temporal data. [18] attempted to capture existing action recognition datasets using DVS cameras. However, video reproduction failed to capture the event
3

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1. P.Pnchhig pushing hahiiggs
A.cction Recogaition
Evew Canara (l09)
Enviornment Seniin
Heuguard Incafication
(inght)
Tast
Fvent
Capion
345
3.Pose Extiaatio
The flow of the data acquisition process. We employed two DVS cameras, positioned on the left and right sides, respectively. Following the recording, the DVS outputs an event stream for pre-processing. This processed data was then employed for three distinct tasks: action recognition, temporal action localization, and pose estimation.
Figure 2:
characteristics in natural scenes, especially motion blur caused by high-speed motion or significant changes in illumination conditions. Constructing a dataset suitable for violence detection requires capturing data with complex actions, fast movement, and occlusion, which existing datasets are not explicitly designed for. In Table I, we provide an overview of several DVS datasets, where #Event Count means the total number events of the dataset and the average number of each example.
Violent Dataset The construction of appropriate datasets for detecting violent actions are crucial to promote research in automated detection technology. [25] proposed dataset created by extracting clips from short films comprising only 200 video segments. [26] collected 1,000 data samples by capturing snippets from hockey games. [27] gathered realistic scenes involving multiple groups engaged specific actions. [28] compiled the RWF-2000 dataset by amassing 2,000 sample clips from the internet. [29] extracted segments from Hollywood movies to create dataset.
Spiking Neural Networks (SNNs) are models that simulate the behavior of neurons in the brain. In contrast to Artificial Neural Networks (ANNs). SNNs transmit signals through discrete spikes, and the accumulation of membrane potentials in SNNs allows them to handle time series data effectively. making them well-suited for processing event-based data. However, due to the non-differentiabiliy of spike sequences, applying the traditional backpropagation (BP) algorithm directly to training SNNs poses significant challenges. As result, various methods have been proposed to explore effective training approaches for SNNs [30, 31, 32, 33, 34, 35, 36, 37, 38].
Privacy-Preserving Action Recognition Image or video editing stands as the most prevalent method of ensuring visual privacy. These methods
be broadly categorized into the following three: Filtering: This involves techniques like spatial downsampling, blurring, and pixelation of images [39, 40, 41, 42). Intuitively, these methods are effective preserving privacy. However, by treating all information uniformly, the removal of private data can significantly hinder accurate action recognition. Empirical Obfuscation: This method masks privacy-sensitive information irrelevant the primary task. Examples include using object detection or segmentation to edit or eliminate faces or bodies [43, 44]. Its efficacy depends on the detector's 's performance and the adaptability between the target and source domains. Information outside of empirical information might still be left exposed. Learning- based Obfuscation: This approach balances task performance and privacy protection. actively suppresses sensitive attributes within visual data via adversarial learning [45, 46, 47). However, for effective privacy assurance, training such model demands substantial amount of computational power.

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Bullying10K Dataset
In this section, elaborate on the acquisition process, preprocessing methods, and annotation details of the Bullying10K, dataset. Simultaneously, we analyze multiple attributes of the dataset, including its temporal length, keypoints motion, and spatial event distribution.
3.1 Data Acquisition
Environment Setting For data collection, we utilize two Davis346 [48], a high-speed event camera that captures pixel brightness changes with microsecond precision. Each pixel's brightness change triggers event (f, p),), where (z. y) represents the spatial coordinates of the pixel, & denotes the event's time, and p is either or reduction). To capture multiple viewing angles and ensure diversity in the collected data, we position two DVS cameras on the left and right sides of the filming scene, as depicted in Figure 2. For consistency in the dataset, the cameras were positioned 5 meters apart, with both camera lenses were oriented at a 30-degree angle from the direct front. Additionally, To capture a diverse range of data and to more closely align with real- world conditions, set up two lighting conditions: light and dark. The sensitivity of the DVS cam- eras allows for time precision of less than lus, which results in more noise under dark ditions compared to light ones. The camera lenses have fo- cal length of 4mm, an aperture of 1.6, and exposure time of 20ms to ensure an appropriate filming range and exposure. we invited 25 distinct participants, leading 50 recording groups in total. The gender ratio among participants was 1:1. Instead of merely repeating a specific ac- tion, and participants were en- couraged to execute movements freely while ensuring the action type remained consistent, adding to the dataset's diversity and complexity. Each event segment contains two participants who as- sume the roles of perpetrator and a victim during segments in- volving violent actions. In trast, sections depicting friendly
1, indicating the polarity of the brightness change (enhancement
Hight
punching
haingrates Hight
pushing daack
pusching Righht
slapping J. Hight
stopping R dark
traggingg L Light
stragglling light
Kicking light
Ricking light
hundshake dark
handolake J.ddrr
greeting J.Jight
Agorguess J.uhgh
walking R_igght
Figure 3: Visualization of human pose keypoints labels on event frames. The labels denote the action name, camera position, and lighting conditions.
actions involve the cooperation between the two participants. Actors instructed perform specific action in each video segment, and we collect ten valid clips for each action. The duration of each sample segment is action-dependent, ranging from 2 20 seconds.
Preprocessing The Davis346 camera directly outputs data in the aedat4 file format, specifically designed for storing event streams. To facilitate subsequent processing and analysis, we transform the raw data into the widely used npy format. npy is common file format for storing NumPy [49] array data, enabling effortless preservation and recovery of multidimensional data, matrices, and other data structures. Throughout this transformation, we organized the event stream into 10-millisecond units to maintain temporal precision while effectively compressing the data. This strategy improves convenience and reduces file size. For user-friendly data manipulation, supply code that merges the event stream into frames and reads the data.
Quality Control To ensure data quality and usability of the data, we marked the position of each camera before the start of filming, along with the relevant settings of the DVS camera (including aperture, focal length, etc.), and maintained consistency in these settings for each capture. To enhance

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data redundancy and robustness, we introduced a manual screening step to optimize the data collection process. We captured twelve sample segments for each action group. Following data collection, we conducted manual screening to exclude poorly captured segments, using only ten segments per group for the final dataset. This ensures that every sample segment in the dataset possesses good quality, facilitating subsequent research and analysis.
3.2
Data Annotation
3.2.1
Category Label
Opx 2px
2px 4px
4px 6px
6px 8px
8px 10px
0.63
0.54
0.45
0.36
0.00
Nose
LEve
REye
LEar
REar
LShoulder
AShoulder
LElbow
RElbow
WWrist
AWrist
LHlp
RHip
UKnee
Aknee
LAnkle
RAnkle
Mead
Neck
Hip
LBig Toe
RBigToe
LSmallTog
RSmallta
LHeal
RHeel
(6)
(C)
punching
pushing
hairgrabing
finger g
strangling
0.50
0.49
gganchin
Pumming
Regen
Stramtnng
gpaeing
hardshaa
Soaping
greeting
handshaking
slapping
walding
kicking
2000
0.00
17500
6.17
15000
0.14
12500
8.12
10000
0.6
0.09
0.1
5000
0.03
500
(d)
(c)
(f)
(g)
Figure 4: Statistical data and analysis of Bullying10K, 10K. (a) motion of keypoints. (b) the ratio events in different polar. (c) distribution of events. (d) loU of individual. (e) distribution of frame number. (f) distribution of event numbers. (g) event number in the sample.
We conduct detailed classification and annotation for each sample after filming for action recognition tasks to identify the represented action accurately. Our dataset consists of ten actions, including six violent actions (punching, kicking, hair grabbing, strangling, pushing, and slapping) and four friendly actions (handshaking, finger guessing, greeting, and walking). further organized each category based subjects, lighting scenes, and camera positions. Each group is named using the subject's code, action name, illumination, and camera position.
3.2.2 Pose Estimation
Pose estimation is task that involves identifying person's 's body position and keypoints in video or image. Precise pose estimation facilitates effective action recognition, making an essential precursor to subsequent action recognition tasks. To acquire human pose data for each DVS video

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segment, we simultaneously leverage the RGB data captured with the event data. The same camera captures both data types and offers overlapping scenes with highly consistent content. This advantage allows us to employ well-established pose estimation algorithms to predict the RGB dataset and obtain initial human pose labels. Specifically, we utilize AlphaPose [50] as an automated labeling tool, multi-person pose estimation system. We employ the ResNer50 [51] backbone pre-trained on the Haple dataset, while the annotation process involved using the YOLOX [52] algorithm trained on the COCO dataset [53] an object detector. Our annotation target includes 26 keypoints of the human body, as specified in [50], and the label information is saved in the COCO format. Upon obtaining initial labels, manually calibrated the labels, illustrated in Figure 3. is important to note that direct pose estimation algorithms for DVS data are still in the early stages of development. Moreover, due to the inherent characteristics of DVS data, the events do not explicitly represent human poses, which compounds the complexity of directly utilizing DVS data for pose annotation.
3.3 Data Analysis
The Bullying10K dataset encompasses 10,000 sample clips, each with duration ranging from 2 to 20 seconds. contains an amount of 12 billion valid events, resulting in a total data volume of 255 GB. Figure (e.f.g) presents the distribution of frames, events, and events per frame in each sample clip within the dataset. Notably, the average sample length of Bullying 10K surpasses existing event datasets captured with DVS cameras. This addresses the limitation of shorter datasets captured by DVS cameras, providing new challenges in establishing long-range dependencies between event data. Figure 4 (a) displays the results obtained by analyzing the movement distance of keypoints between consecutive frames. Different key- points exhibit distinct motion distribution pat- terns, with the most prominent trends observed in the wrist and elbow movements, aligning with human motion characteristics. To quantify the degree of task overlap in the video, we calcu- late the Intersection over Union (IoU) of the bounding boxes for two characters appearing in the same frame. Figure 4 (d) visualizes the distribution of loU values, which are primarily concentrated between 0.1 and 0.5. This indi- cates significant number of instances where characters overlap within this dataset.
0.6
0.4
0.2
0.0
As illustrated in Figure 4 (b), our analysis re- veals a slightly lower occurrence ratio of posi- tive and negative polarity events. Moreover, we visualize the ratio of positive and negative events for each action category within the dataset. As shown in Figure 4 (c). The movement distribution of actions such as punching and strangling predominantly occurs the upper part of the image, while walking exhibits higher likelihood of occurring in both the upper and lower parts of the image.
Figure 5: PR curves for each category obtained from the X3D model trained on Bullying 10k.
4
Evaluation and Task
We have provided three benchmark tasks: action recognition, temporal action localization, and pose estimation.
4.1 Action Recognition
Experimental Setting The action recognition task aims to predict the corresponding action labels based on input images. Each sample segment in the dataset includes single behavior, making suitable for single-label classification tasks. During preprocessing, the event streams are combined into frames, forming a sequence that then used to perform the action. The Bullying 10K dataset has been divided into training and validation sets with an 8:2 ratio, and the temporal unit for integrating the event stream is set to 10 ms. We provided corresponding interface in the code to ensure data consistency. Given many frames and the need for batch training, randomly crop video
7

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Table 2: Performance of different action recognition models on Bullying 10k.
Model
backbone
step
step
step 16
gap 0
gap 2
gap 4
gap 0
gap 2
gap 4
gap 0
gap 2
gap 4
C3D [55]
Conv3D
47.60
49.05
52.10
51.70
54.75
57.20
60.35
68.55
71.25
TAM [56]
ResNet
54.85
59.20
62.00
57.85
60.80
65.05
58.20
66.90
71.20
R2Plus ID [57]
ResNet18
54.60
57.15
60.15
56.50
61.60
65.45
58.20
65.70
69.25
R3D [58]
ResNet18
57.20
61.05
62.10
58.90
65.46
68.60
62.70
69.75
72.50
SlowFast [59]
ResNe150
57.80
60.30
61.55
60.10
66.50
70.90
61.70
70.55
74.00
X3D [60]
ResNet
60.30
62.40
64.80
63.30
69.40
72.15
65.75
72.45
76.90
SNN [61]
SEW-ResNet19
51.75
53.00
53.85
56.40
59.50
62.85
58.48
64.05
67.05
Table 3: Performance of different privacy-preserving action recognition models on Bullying
Model
RGB
DVS
DS-2 [47]
DS-4 [47]
GB-3 [45]
GB-5 [45]
BDQ-1 [62]
BDQ-2 [62]
R3D [58]
64.00
66.80
63.30
63.15
62.70
61.45
60.10
59.75
SlowFast [59]
59,25
69.00
57.80
55,40
57.20
54.95
60.20
59,45
X3D [60] 63.20
70.80
60.75
52.25
58.20
47.80
67.15
65.60
segments to a fixed length, sampling at O, 2, and 4 intervals. We employed various commonly used action recognition models to evaluate the performance of the Bullying 10K dataset. We explored the performance spiking neural networks on this dataset, showcasing their potential for action recognition tasks.
Evaluation Metric In the classification task. we measure the performance of the network by calculating the accuracy of the output corresponding the labels. However, misjudgment of violent events can have severe consequences and cause irreparable harm. Violent scenarios occur less frequently than non-violent ones, implying a substantial class imbalance real-world situations. Relying solely accuracy an evaluation metric may not adequately reflect the model's 's ability to predict violent events accurately. To address this issue, we have employed the Precision-Ree Recalll (PR) curve [54]. The PR curve allows us examine the model's predictive capabilities across varying judgment thresholds.
Results and Analysis Table presents a detailed comparison of the performance of several widely used action recognition models on the Bullying dataset. It includes the backhone architectures of each model and their respective operational configurations. We conducted experiments using frame intervals of 0, 2, and 4, with three different temporal step lengths of 4, 10, and 16. The results demonstrate that increasing the frame interval and temporal step length contributes to improved precision of the models. However, even models that exhibit strong performance in traditional visual classification tasks did not yield satisfactory results on the Bullying 10K dataset, indicating the dataset's s unique challenges. Furthermore, performed visual analysis of the corresponding PR curve of the X3D model on the Bullying 10K dataset. The PR curve reveals significant variations in performance across different action categories, underscoring the dataset's complexity and the need for robust recognition models.
We implemented different privacy protection methods on the corresponding RGB frames of the Bullying 10k dataset. We observed that after employing privacy protection with RGB data, there is a decline in performance. However, DVS data demonstrated slightly superior results compared to RGB data. DVS inherently offers advantages such as resilience to significant illumination changes, motion blur resistance, and reduced data redundancy. Therefore, for tasks like action recognition that rely on motion characteristics, the features captured by DVS are particularly beneficial.
4.2 Temporal action localization
Experimental Setting In surveillance videos, violent scenarios often occur irregularly and sporadi- cally. Due to the few background disturbances and noise in event data, we can naturally concatenate multiple samples to form extended sequences. In our task, we input a video frame sequence that encompasses segments from different action categories randomly extracted from the dataset and

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Table 4: Performance of different temporal action localization models on Bullying 10k.
Model
Feature
AUC
ARâi
AR
AR&10
AR 100
BSN [67]
TSN [63]
75.9
31.1
70.5
75.8
77.6
BSN BMN [68]
TSM [64]
75.5
31.8
71.2
75.8
77.2
TSN
80.3
34.6
74.3
81.0
82.0
BMN
TSM
81.0
36.0
75.3
82.4
82.7
Table 5: Performance of different pose recognition models on Bullying1OK:
Model SispleBaseline [69] SimpleBaseline [69]
HRNet [70] (HRNet-ws32)
HRNet [70]
SimpleBaseline [69]
(ResNet-50)
(ResNet-101)
(HRNet-ws48) (Spiking ResNet-50)
AP
62.6
63.6
62.7
62.8
54.1
Ap2o
88.3
88.2
88.2
87.8
84.6
AP75
67.4
67.7
67.5
67.2
57.6
Apd
58.3
59.3
58.6
59.5
49.3
APL
73.4
74.6
73.9
74.9
64.9
AR
65.9
67.0
66.3
66.7
58.5
integrate them into longer video sequences. And aim to predict the action label along with its corresponding start and end times. The ground truth annotations constructed based on the re- spective time intervals. For training on the Bullying 1OK dataset, we have chosen commonly used temporal action localization models. To achieve more accurate testing results, we provided action recognition data for the pre-trained features at the same ratio. Additionally, we trained the model for the localization task 1:3 ratio. Initially, action recognition task, which serve as the feature extractors for the localization models. We extract features from the dataset and store them for subsequent analysis and processing.
pre- pre-trained the TSN [63] and TSM [64] models the
Evaluation Metric Average recall [65] and Area Under the Curve (AUC) [66] are commonly used metrics evaluate action localization. Concurrently, we employ the AR @ indicator for assessment, representing the recall rate under the condition of N proposals. This study considers N to be 1, 5, 10, and 100. Additionally, we calculate the AUC for the AR-AN curve.
Results and Analysis We present the accuracy of several commonly used temporal action localization al- gorithms on Bullying 1OK for compar- ative evaluation. They exhibit varying performances under different features. On the other hand, performance signif- icantly diminishes with the reduction of proposals. It is worth exploring model designed for processing event datasets that maintain higher pre- cision with fewer proposals.
4.3 Pose estimation
Experimental Setting Human pose estimation typically involves provid- ing an image or video containing one or multiple individuals and outputting the corresponding locations of vari- ous keypoints for each person. These keypoints include the head, shoulders, arms, and legs. Different datasets may have different numbers of joints, and in the case of Bullying 10K, use 26
Figure 6: Taking the prediction results of the SimpleBaseline model with ResNet101 the backbone, visualize the skeleton and heatmap of the prediction results, respectively.

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keypoints. Pose estimation can provide additional information for action recognition. We split the dataset an 8:2 ratio and have provided the corresponding pose annotations along with the data to ensure consistency. We evaluate the performance of Bullying using several commonly used pose estimation models.
Evaluation Metric The standard metric used to measure pose estimation is usually based on Object Keypoint Similarity (OKS) [71]. OKS is scale-invariant measure of localization accuracy and is utilized to evaluate how closely the predicted keypoints of model match the ground truth keypoints. OKS = Ex (2
where d; is the distance between the position of predicted keypoints and the ground truth. denotes the visibility of related keypoints. indicates the object scale. A; is the predefined constant that controls Averrge Precision (AP) and Recall (AR) used, including AP, A (OKS is at 0.50), .APM ,PM,APl, to measure the accuracy of different models on the Bullying 10K dataset.
Results and Analysis Table 5 presents the results of multiple commonly used pose estimation models on the Bullying dataset. However, these models exhibit relatively low accuracy on the dataset, indicating that Bullying10K poses substantial challenges for pose estimation tasks. Additionally, although HRNet has shown superior capabilities to SimpleBaseline on other RGB datasets, it achieved lower accuracy our dataset. This discrepancy may be attributed to the unique nature of our event-based dataset, which significantly differs from RGB images. Furthermore, we investigated the potential of SNN in posture estimation by testing the SNN backbone using the SispleBaseline algorithm. To enhance the interpretability of the results, we visualized relevant output images and presented heatmaps for selected samples.
5
Discussion
This research introduces novel event-driven dataset called Bullying10K, which utilizes Dynamic Vision Sensor (DVS) cameras to detect instances violent behavior while preserving individual privacy. This approach offers novel possibility for privacy preservation, distinct from traditional image surveillance methods. It has an influence in the field of privacy protection and security surveillance.
However, typically, the kind of private information that cameras can capture varied and encompasses aspects like facial details, gait patterns, and even individuals' habits of daily life. Some recent technologies can identify specific individuals using non-facial information [72, 73. 74]. Our dataset might struggle to prevent leaks of non-faciaa information, such as gait data. Still, we wish to emphasize that facial data is the most commonly used and has been incorporated into many crucial applications. This type of privacy is our primary focus in this paper.
Moreover, for the reason for clarity in labeling and for comparability during model validation, our dataset defines specific, commonly observed actions. It's challenging to cover every possible violent or non-violent action a person might exhibit comprehensively. However, an incomplete category set might increase the risk of classification errors in the model, which could adversely affect judgments regarding violence.
The dataset addressed the limitations of existing event-driven datasets by featuring complex, rapid movements and overlapping figures, presenting higher complexity and challenges. By offering large-scale dataset, Bullying 10K enables researchers to explore complex actions and contributes to advancements in violence detection and presecypreservvttion techniques.
Acknowledgments and Disclosure of Funding
This paper is funded by the National Key Research and Development Program (Grant No. 2020AAA0107800).
10

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15


Reviews:
[{"review_id": "jO5Yl5Kx6t", "reviewer": "Reviewer_4C71", "summary": "", "questions": "", "limitations": "I could not find the corresponding statements about the limitations of the proposed Bullying 10K. I do think that the authors should provide discussions about the potential societal impact and the limitations of this work itself.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The dataset provided in this paper is designed to address the privacy issues in the training of sensitive action detection models, and the dataset itself has the advantage of being desensitized, which is larger and more complex than the existing dataset and provides data support for the violent action recognition system based on the desensitized dataset. It has a positive social influence in the field of privacy protection and security surveillance.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "wYjWVl8Hsn", "reviewer": "Reviewer_MGZy", "summary": "", "questions": "", "limitations": "- It raises doubts about the practicality of such machine vision techniques in terms of privacy-preservation and cost-effectiveness. The development of privacy-preserving machine vision has been ongoing for several years, with a substantial body of literature available. However, this study fails to address or discuss any of these existing approaches.\n- In fact, privacy-preserving vision methods encompass various approaches such as using blurred images, low-resolution images, and radar images. Recently, multiple approaches employing privacy-preserving lenses have been proposed to protect privacy while recognizing pose and action. To substantiate the key claims made in this paper, it is crucial to qualitatively and quantitatively compare the advantages and disadvantages of DVS-based action recognition in contrast to these various privacy-preserving machine vision techniques. When considering real-world deployment scenarios, the necessity for multiple camera installations and preprocessing makes the DVS-based approach appear cost-inefficient compared to other privacy-preserving machine vision technologies.\n- DVS-based computer vision was developed to address the issues of data redundancy, high latency, and low temporal resolution associated with traditional RGB image-based computer vision techniques. There is concern that the main argument put forth in this study is merely a reinterpretation, suggesting that DVS-based datasets are also superior in terms of privacy preservation.\n- The publicly available dataset can be downloaded from the website and has a total size of approximately 46GB. It is unclear why the paper consistently refers to a dataset size of 100GB.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- As asserted by the authors, neuromorphic dataset-based action recognition can be considered as an alternative capable of recognizing actions without compromising privacy.\n- The structure of the paper is relatively well-organized, and there were no issues in comprehending its content.\n- Performance evaluation was conducted on various models and backbones.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "7rbQce4b8d", "reviewer": "Reviewer_W6Gq", "summary": "", "questions": "", "limitations": "See \"Opportunities for Improvement\", there's a need to discuss a concrete definition of privacy.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "Using cameras in public spaces to detect and prevent violence is beneficial, but privacy concerns arise. The Bullying10K dataset introduces a novel approach by utilizing Dynamic Vision Sensors (DVS) to capture pixel brightness variations instead of imagery, addressing privacy issues. This innovative concept opens up new possibilities as previous DVS implementations were limited in scale and simplistic labels. With the Bullying10K dataset, complex and rapid actions can be effectively encompassed.\n\nExperimental results on action recognition, temporal action localization, and pose estimation demonstrate the efficacy of Bullying10K in these tasks. It provides a valuable opportunity to develop more advanced algorithms for these specific areas.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "b17qtDoVlh", "reviewer": "Reviewer_dVaU", "summary": "", "questions": "", "limitations": "Unfortunately, it also seems as if the authors did not properly account for ethical, privacy, and societal aspects. Basically, large parts of the respective \"argumentation\" in the paper as well as in question answers in the the supplementary material boil down to \"DVS footage is privacy-preserving in and by itself, given that we don't see faces.\" For instance, the answer to question A.2.15 more or less negates the identifiability of natural persons in the dataset. The same argument can also be found around lines 35-40. This is by far not sufficient and not in line with long-known research on identifying individuals based on movement (e.g. gait) patterns. \n\nThe same rather un- or only superficially reflected tenor also applies to aspects such as the potential negative societal impacts (e.g., checklist question 1c). The question is whether the authors discussed any potential societal impacts of their work. We are in the domain of video surveillance and automated behavior detection. And the authors **just go with a N/A answer?** Im mean... there is a reason why these questions are asked. Different from the statement in the checklist (2b), the limitations **of the presented dataset/approch** are also not discussed - not even superficially. Similarly, I also have my doubts regarding the consent gathered from the students. In the supplementary material, the authors state that consent was collected, but we do not know whether the consenting students were actually aware of the potential risks and impacts of consenting. It is also not 100% clear whether they only consented to being recorded or actually also to having the resulting data mad publicly available. Long story short: Given various warning points, I am not convinced that the authors actiually did their homework in matters of ethics, privacy and societal aspects but rather took respective procedures overly easy.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Substantial dataset\n- Technically, the paper is mostly reasonable, but not overwhelming.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: **Preface.** The average score of this paper is currently 5.75 (7, 6, 5, 5), which is below the acceptance threshold. However, I am taking into consideration the overall quality of the work and the fact that the reviewers with scores 5 (4C71, MGZy) did not return for the rebuttal discussion despite reminders. The authors have comprehensively addressed the comments of these reviewers and incorporated the changes into the revision as I discuss below, and the reviewers have also noted the potential positive impact of this paper, so if the reviewers had even raised their score to a 6, the average score would be over the acceptance threshold. As such, I am consequently considering the paper quality and content of the discussion in my recommendation to the SACs and PCs beyond the numerical score. 

**Summary.** This paper proposes Bullying10K, a dataset containing acted violent and non-violent events for supporting violence detection in a privacy-preserving manner. The specific gap that this work addresses is the lack of a dataset that simultaneously considers violence detection *and* privacy preservation. 

**Strengths / Reasons to accept.** The reviewers have noted that the paper is structured and well-organized (MGZy), has a positive social influence in the field of privacy protection and security surveillance (4C71), is substantial (dVaU), and provides a valuable opportunity to develop more advanced algorithms for these specific areas (W6Gq).

**Weaknesses / Reasons to reject and author response.** The primary reasons as enlisted by the reviewers are as follows:
- *Details of the capture setup / data collection*. 4C71 pointed out the lack of details on camera setup, protocol, participant composition, and environmental factors. I find that the authors have adequately addressed these in the revised Sec. 3.1.  They have also added the exact informed consent form to the Supplement. 
- *Contextualizing the scope of privacy considered*: 4C71, W6Gq, and dVaU raised points about the notion of privacy in this work and whether it is preserved in the data construction. The authors have contextualized this adequately in my opinion, both here and in the revised manuscript. While behavior as biometrics remains a developing research area, the revised contextualization does not diminish the usefulness of this dataset in my opinion. 
- *Experiments comparing against other privacy-preserving methods*. 4C71 and MGZy raised concerns about comparing against privacy-preserving techniques for action recognition. The authors have added experiments to revised Sec. 4.1 and the results support the argument for the utility of the DVS data.
- *Societal impact and limited non-violent categories*. dVaU mentioned the lack of discussion of potential negative societal impact and the issue of limited non-violent categories. The authors have acknowledged these and addressed them in the discussion. As it stands, the provided categories still make this a useful dataset for the community.

**Final recommendation.** Given these improvements to the manuscript, I believe the concerns as stated by the reviewers have been addressed and a raised score is warranted. As such, the benefits of the dataset to the community now outweigh the stated concerns. There are remaining issues (see below), which I believe can be addressed in writing and are insufficient for rejection. 

**Recommendations to authors.** Authors, while I am recommending accepting this paper, I do so with the caveat of incorporating the following suggestions:

- **Your Datasheet is a bit thin on details.** 
  - You've incorporated details about the data collection (response to 4C71) in the main paper, and yet have not mentioned these details (camera details, positions, participants, etc.) in the Collection part of the Datasheet. These exist for a reason, and I suggest you be comprehensive in describing the details. 
  - Moreover, given your acknowledgment of dVaU's points of societal impact, I find your Section on Uses to be puzzling. A response of "No" to A.4 and A.5 in light of your discussion of the potential negative societal impact on violence detection is unacceptable. I recommend you update these with a detailed description of potential risks. 
  - An ethic's board review should have been done for this paper. I suggest adding a discussion on compliance with China's PIPL. 
- **Please re-cite the references** from the rebuttal discussion back in the paper discussion / datasheet. In your response to W6Gq you contextualize the role of faces as a privacy threat with references, and seem to have added these to the introduction. However, your mention of data leakage in the Discussion and A.2.15 is again thin, and could use replicating the discussion on various means of data leakage, including, but not limited to, mentioning the work on gait detection you discuss in the rebuttal.
Decision: Accept (Poster)