WNet: Joint multiple head detection and headpose estimation from a spectator crowd image
Yasir Jan[0000−0002−9232−1703] , Ferdous Sohel[0000−0003−1557−4907] , Mohd Fairuz
Shiratuddin[0000−0002−9529−6320] , and Kok Wai Wong[0000−0001−8767−1031]
Murdoch University, Perth, Australia{Y.Jan, F.Sohel, F.Shiratuddin, K.Wong}@murdoch.edu.au

Abstract. Crowd image analysis has various application areas such as
surveillance, crowd management and augmented reality. Existing techniques can detect multiple faces in a single crowd image, but smallhead/face size and additional non facial regions in the head boundingbox makes the head detection (HD) challenging. Additionally, in existinghead pose estimations (HPE) of multiple heads in an image, individual
cropped head image is passed through a network one by one, insteadof estimating poses of multiple heads at the same time. The proposed
WNet, performs both HD and HPE jointly on multiple heads in a single crowd image, in a single pass. Experiments are demonstrated on the
spectator crowd S-HOCK dataset and results are compared with the
HPE benchmarks. WNet proposes to use lesser number of training images compared to number of cropped images used by benchmarks, anddoes not utilize transferred weights from other networks. WNet not just
performs HPE, but joint HD and HPE efficiently i.e. accuracy for morenumber of heads while depending on lesser number of testing images,
compared to the benchmarks.
Keywords: Head detection · Head pose estimation · Crowd Analysis.

1

Introduction

Research in crowd analysis is focused on various aspects of crowd such as crowd
counting [9], [26], face detection [15], [16], body detection [19], [5] and HPE[15], [16], [19], [2], [1]. These tasks become more challenging with the increasing
number of people in the crowd, body occlusions and the low resolution features[26]. This paper focuses on joint multiple HD and multiple HPE of small sizedheads present in a crowd image.
Previously, research has aimed towards single task of face detection [8], HDor HPE, while others propose joint HD and HPE [15], [16], [2], [1], [22], [24] butwith few limitations. Some techniques can do face alignment and HPE in a jointmanner [24] but are applicable for images with single heads only. Other techniques do multiple HD but do not perform multiple HPE [15], [16], [19]. Multiple
HPE is done by cropping individual faces/heads and then individually estimating the pose of each face/head one by one. Head dimensions is also an issue for2

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some techniques [15]. They can detect faces of varying dimensions but perform
weakly in small sized faces, because they are dependent on other pretrained networks [6]. To detect small sized faces upscaling of low resolution images to highresolution images is proposed [6], which also increases computation. Yet thesearchitectures are only applicable for single HPE at a time. Therefore, as far as
we know, there is no existing technique which performs joint HD and HPE of
multiple small sized heads.
The proposed WNet architecture, consists of two cascaded UNets [17], [7],followed by a grouping module. Previously, UNets have been used for imagetransformation techniques such as image de/colorization, sketch to image conversion, aerial image to map generation, facades generation, image de-noising/ de-snowing, image segmentation and vice versa [17], [7], [23]. In tasks suchas aerial to map generation and facades generation, detailed aerial and facade
images are transformed into simplified unicolor regions. Based on these previous
ideas, in the proposed architecture, UNets are first used to transform cluttered
crowd images into simplified color blocks based images. These simplified images
give a better visual understanding of the scene rather than the original clutteredscene. Therefore, using the transformed simplified images, simple color searchand euclidean distance based techniques can be utilized to do HD and HPE.
The main contributions in this paper are as follows:– To the best of our knowledge, this is the first CNN based technique toperform joint HD and HPE of multiple heads.
– It does not need each head to be cropped and passed to the network individually for HPE.
– WNet generates intermediate secondary output images, which reduces thecomplete black box effect of the end to end network pipeline.
The rest of the paper is organized as follows. Section 2 discusses the previous
work related to head detection and head pose estimations. Section 3 discusses our
methodology, as well as architecture. Section 4 discusses the S-HOCK dataset.
Section 5 explains our experimental protocol while in Section 6 the results areshown. Section 7 concludes the paper, while Section 8 discusses the future possible improvements in this work.

2

Related Work

There are various CNN based approaches for face detection [15], [16]. Sometechniques are focused on single person face datasets [13], and do not target
crowd images. For multiple face datasets, such as WIDER Face [25], CNN based
networks trained on the Imagenet dataset, are used for face detection. Since
Imagenet have dimensions larger than 40 pixels [6], therefore, these networks
cannot successfully locate and identify objects of smaller dimensions [6], [10]. For
detecting images with tiny faces, image upscaling [6] is proposed, so that smaller
faces become bigger in size and are then detected. Another solution is to have a
network which can detect small dimensional objects, rather than images scaled

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to higher dimension and then detecting. Instead of upscaling, “super-resolved”
CNN features [10] can be used to detect small objects. Super-resolving convolves
small object CNN features and makes them similar to large object CNN features.
Other than HD, some techniques do body detection as well, in which network can
locate center of body parts, including heads [3]. Such body detection techniques
may locate heads but are not aimed for HPE.
HPE is achieved using hand crafted features as well as deep learning based
approach [15], [11], [14]. Deep learning based approaches in this aspect are very
scarce as research is focused on facial landmark detection which indirectly is usedfor HPE [16], [18]. In low resolution images, the techniques depending on facial
landmarks fail [18]. Therefore, HPE techniques should be independent of facial
landmark detection. Existing HPE techniques have various other limitations.
They are applicable to single person images, or they focus on multiple persons
by individually cropping each face for pose estimations [15], [16]. Multiple facespose estimations are not performed in a single forward pass of the network.
The network [15] proposes to fuse intermediate layer features and train separate sub networks for face detection, landmark localization and pose estimation.
Similarly, [18] proposes to train a separate sub network for each head angle
separately. Both these networks are aimed to calculate single head angles at atime, therefore cannot perform multiple HPE at the same time. UNets have also
been used previously in a cascaded/stacked manner[4], [20] , but for different
applications.

3

MethodIn the proposed WNet method, we follow a 3 step approach, as shown in Figure
1. First step is to transform a crowd image into a less cluttered image. Aim is toremove all the non head pixels from the image, because they may cause clutter
and errors in further steps. Therefore at first step a UNet [7] converts a crowd
image to a head-region-masked (HRM) image. The generated HRM image will
have all the non head pixel values suppressed to 0, while other head region pixels
remain unaffected. Second step is to have an image with color markers. To reduce
the effect of color variation in HRM heads, the HRM image is first converted tograyscale and then passed onto second UNet block. The second UNet generates
a color coded head (CCH) image from grayscaled HRM image. In CCH image
individual head regions and their centers are marked with different colors. The
centers are marked with 3 × 3 white pixels, while the head regions are coloredbased on their pose. In the third step, CCH image is used and based on colors
and euclidean distance, the head centers and the head regions are identified.
Based on the color of the head region head poses are also identified. The detailsof the architecture is explained below.
3.1

WNet Architecture

The proposed WNet architecture, is composed of two UNet blocks, followed bya grouping module, as shown in Figure 1. Each UNet block layers are similar

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Fig. 1. The proposed WNet architecture, consists of two UNet blocks [7], followed bya grouping block. The UNet blocks transform the original image to a color coded head
image. The grouping block locates the marked head centers, then bounding boxes andthen head poses. (Best viewed in color)

to [7] . Each block consists of 8 encoder and 8 decoder layers. The numberof output filters at each encoder layer are in the sequence: 64, 128, 256, 512,
512, 512, 512. There are skip connections between encoder and corresponding
decoder layers. Therefore at each decoder layer input, the output from previous
layer is concatenated with the corresponding encoder layer output. The numberof filters in the decoder are in the sequence: 512, 512, 512, 512, 256, 128, 64, 3.
Encoder layers are activated with LeakyReLU while decoder has ReLU activation functions. At all layers filter size is 4 × 4, with stride 2 and padding 1 ineach direction. Both UNets are trained separately, using conditional Generative
Adversarial Networks (cGANs), similar to [7].
UNet block 1: The first UNet block is trained to convert a cluttered RGB
crowd image into a simplified HRM RGB image. The output HRM image is
generated by suppressing all R,G,B channel values of the background and bodypart pixels, and making them 0. The head region pixels remain unchanged. The
HRM image is then converted to grayscale by averaging all R,G and B channel
values. As a result the inter channel color variation is finished and all the heads
become almost similar in color, irrespective of their original color shades. This
reduces the negative effect caused due to skin or hair color.
UNet block 2: The second UNet block takes the grayscaled HRM image as
input, and an output CCH image is generated. In CCH image different head posewill be represented by activating only a combination of RGB color channels, andsuppressing the rest to value 0. For example, a right pose head region will have
only R channel activated, while G and B channels are suppressed to 0. Similarly,
a left head pose will have only G channel activated and rest of the channels will
be suppressed to 0. Therefore, for the five head poses i.e. right, left, front, awayand down, the respective activated color channel combinations used are R, G,
B, RG and GB respectively. Additionally, a 3 × 3 white pixel block is created in
the middle of each head region, which is later used to identify the head center.

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Therefore, each head’s pixels in the input image are replaced with the channel
selected head’s pixels with an additional white pixel block in the center of head.
Grouping: The CCH image generated from the previous UNet block is used to
extract the head centers and head pose. This step can be divided into 3 sub steps.
(i) The mid positions of 3×3 white pixels in the image are extracted. It identifies
the head centers. Noise correction is done, by averaging head centers within thehalf head size range. (ii) Group all the head pixels, which are around theircenters within the average half head size range. The minimum and maximum
pixel positions of each group gives the bounding box values of that head. (iii)
Identify head poses of each head. In a CCH image, all the pixels within the headbounding box are color coded based on the pose. Therefore, the color codes of
all the pixels within the head region are aggregated, and the maximum color ischosen to identify the head pose.
WNet generates simplified uncluttered crowd images at intermediate steps inthe pipeline before calculating head bounding boxes, while other techniques first
generates numerical values for head bounding boxes and then mark the headboundaries. These simplified images can be useful in applications where exact
head locations are not required but a clear visual understanding of the scene e.g.
crowd surveillance, augmented reality, is required. This intermediate step also
reduces the full blackbox effect of the networks. Each intermediate step of the
network can be fine tuned for further improvement.

4

DatasetThe Spectator Hockey (S-HOCK) dataset [19] is used for the experiments.
It is the only publicly available dataset for spectator crowd with small heads
annotated with head bounding boxes and head poses. It has a total of 75 videos(5 camera views × 15 different matches). Video from only a single camera has
been annotated with head bounding box and head pose values. Each videos is of
31 sec duration is split into 930 annotated frames. Out of the 15 matches, 2 areused for training, 2 for validation and 11 for testing. Each frame has a resolutionof 1280 × 1024 pixels with the dense crowd.

5

Experiments

S-HOCK has a total of 1860 annotated training frames (930 frames of 2 training
videos) and 10230 annotated testing frames (930 frames of 11 testing videos).
To reduce redundancy and also reduce the number of frames, 9 frames out of
10 are skipped. A total of 93 frames are extracted from each 930 frame video(5 : 10 : 930). Therefore the interval between two consecutive frames is approx
1/3 of a second. It should be observed that only 186 frames are used for training
which is far lesser number than the thousands of ImageNet data, and 1023 frames
are used for testing. In 1023 testing frames there are a total of 155085 people,
with average head dimensions of width 36.46 pixels and height 40.69 pixels. The
range of head dimensions in testing images is shown in Figure 2.

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Y. Jan et al.
14000
Width
HeightNumber of people

12000
10000
8000
6000
4000
2000
0
15

20

25

30

35

40

45

50

55

60

Pixel Dimension

Fig. 2. Head dimensions of people in testing images.

Each frame is further sliced into 20 subframes to maintain a resolution of
256 × 256. The proposed WNet architecture consists of 2 UNets, each one ofthem trained separately. Both UNets are trained with their own input output
image pair as discussed below.
UNet block 1: All the 186 frames are sliced into 20 each subframe (totaling 3720 subframes). Input 3720 subframes are paired with output 3720 HRM
subframes, for generating input output training pairs. Training of UNEt block
1 with these input output pairs is done for 30 epochs.
UNet block 2: Block 2 is trained using the grayscaled HRM and respective RGB CCH frames of the 186 training frames of the dataset. Input HRM
subframes are grayscaled and paired with respective RGB CCH subframes, forgenerating input output training pairs. But there is a class imbalance problem regarding the head pose i.e. left pose heads in the dataset are more than
the right pose heads. Therefore, to solve the class imbalance issue, horizontally
flipped 3720 sub frames are added into the training data. It makes a total of
7440 training subframes. Flipped and grayscaled HRM subframes are pairedwith respective flipped CCH subframes, for generating additional input output
training pairs. For flipped images, the color of left and right pose will also be
swapped. The 7440 input output pairs are used for training the UNet2 blockwith 10 epochs.

6

Results

The proposed WNet technique is tested using 1023 testing frames sliced into resolution 256 × 256. The technique first detects heads and generates the boundingbox. Then based on the bounding box it identifies the head pose of each detected
head. After frame slicing, the heads at the edges of the subframes get sliced aswell. HD and HPE results are generated by ignoring the sliced heads. Generally
the Intersection over Union (IoU) of detected head bounding box region andground truth head bounding box region greater than 0.5 is accepted as correct.

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0.9
Precision
RecallF1score0.8

Head bounding box scores

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.2

0.3

0.4

0.5

0.6

0.7

0.8

IoU is greater thanFig. 3. Head bounding box accuracy.

Experiments are performed for multiple HD IoUs i.e. IoU greater than 0.3, 0.4,
0.5, 0.6, 0.7. The precision, recall and f1score of HD for varying IoU is given in
the Figure (3). It achieves more than 0.6 precision, recall and F1 score for HD(IoU greater than 0.5).
The HPE benchmarks mentioned in the dataset paper [19] performs HPE
on 34949 single head instances. An individual head is cropped and then headpose is estimated. Therefore, it takes 34949 cropped head images to generate
the results of 34949 heads. Their accuracy ranges between 0.3 and 0.4, as shownin Table 1. In contrast, the proposed WNet architecture was tested with fewer
number of frames (1023 frames / 20460 subframes). Although in these lesser
testing frames, the total number of heads is 155085 which is ≈ 4.4 times morethan the benchmark number of heads (34949). Therefore, WNet can do HPE for
more heads with lesser frames. Additionally, this technique also performs HD,
not just HPE. HPE accuracy is dependent on the HD accuracy. Therefore, thetable 1 shows the accuracy for correctly estimated head poses with respect tocorrectly detected heads.
The accuracy of different IoU values is greater than 0.32. Our focus is on thenumber of accurately detected heads and head poses which is greater for WNet
compared to other techniques. Results should be compared keeping in view thatthe benchmarks do not perform HD and can perform HPE on single cropped
head. While proposed WNet jointly performs HD and HPE on multiple heads.
Therefore, WNet uses lesser testing data frames and calculates higher numberof accurate head poses.

7

Conclusion

Proposed WNet architecture, can perform joint multiple HD and HPE in a single
forward pass. The technique is based on image to image transformation. Firstsub blocks of the architecture transforms the cluttered crowd images into color
marked simplified images. Using the simplified images, head bounding box and8

Y. Jan et al.

Table 1. S-HOCK dataset benchmark HPE average accuracy compared with WNet
HPE accuracy. Benchmarks are tested on 34949 cropped heads images. While WNet
is tested on only 1023 full scene frames (155085 heads). For IoU = (0.3, 0.4, 0.5, 0.6,0.7), HD ≈ 0.8, 0.7, 0.6, 0.4, 0.2.
MethodAvg Acc. Acc HPE headcount Method type

Orozco [12]0.368
WArCo [21]0.376
CNN [19]
0.346
SAE [19]
0.348
WNet (IoU = 0.3) 0.321
WNet (IoU = 0.4) 0.323
WNet (IoU = 0.5) 0.325
WNet (IoU = 0.6) 0.337
WNet (IoU = 0.7) 0.34

≈ 12861≈ 13140≈ 12092≈ 12162≈ 39825≈ 35064≈ 30241≈ 20905≈ 10545

Only single HPE
Only single HPE
Only single HPE
Only single HPE
Joint multiple HD and HPE
Joint multiple HD and HPE
Joint multiple HD and HPE
Joint multiple HD and HPE
Joint multiple HD and HPEhead pose information are extracted. The proposed technique has been tested on
S-HOCK spectator crowd dataset. Results of HD and HPE show that the proposed WNet technique uses lesser number of frames and accurately estimatespose of more number of heads. WNet is an image to image transformation technique, which is also useful for applications where exact head dimensions are notrequired but simplified crowd images may be useful for a better visual understanding.e.g. crowd surveillance.

8

Future Work

This technique currently aims towards spectator crowd dataset, but it could
b extended for heads/faces in the wild. It could benefit from heads segmented
datasets, which could improve HD accuracy. This may result in sharper headextraction. Additionally a range of colors maybe assigned for a range of headposes which can be correlated. Instead of image to image transformation, headbounding box values can be extracted using other techniques, which can be further used for pose estimation.

9

Acknowledgment

This work was partially supported by an Australian Research Council grant
DE120102960 and Murdoch University grant.

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