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 small head/face size and additional non facial regions in the head bounding box makes the head detection (HD) challenging. Additionally, in existing head pose estimations (HPE) of multiple heads in an image, individual cropped head image is passed through a network one by one, instead of 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, and does not utilize transferred weights from other networks. WNet not just performs HPE, but joint HD and HPE efficiently i.e. accuracy for more number of heads while depending on lesser number of testing images, compared to the benchmarks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asteriadis, S., Karpouzis, K., Kollias, S.: Face tracking and head pose estimation using convolutional neural networks. In: Proceedings of the SSPNET 2nd International Symposium on Facial Analysis and Animation, FAA 2010, p. 19. ACM, New York (2010). https://doi.org/10.1145/1924035.1924046
Bao, J., Ye, M.: Head pose estimation based on robust convolutional neuralnetwork. Cybern. Inf. Technol. 16(6), 133–145 (2016). https://doi.org/10.1515/cait-2016-0083
Cao, Z., Simon, T., Wei, S., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1302–1310, July 2017. https://doi.org/10.1109/CVPR.2017.143
Christ, P.F., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415–423. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_48
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010). https://doi.org/10.1109/TPAMI.2009.167
Hu, P., Ramanan, D.: Finding tiny faces. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1522–1530, July 2017. https://doi.org/10.1109/CVPR.2017.166
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976 (2017)
Jiang, H., Learned-Miller, E.: Face detection with the faster R-CNN. In: 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp. 650–657, May 2017. https://doi.org/10.1109/FG.2017.82
Kang, D., Ma, Z., Chan, A.B.: Beyond counting: comparisons of density maps for crowd analysis tasks - counting, detection, and tracking. CoRR abs/1705.10118 (2017). http://arxiv.org/abs/1705.10118
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S.: Perceptual generative adversarial networks for small object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1951–1959, July 2017. https://doi.org/10.1109/CVPR.2017.211
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 607–626 (2009). https://doi.org/10.1109/TPAMI.2008.106
Orozco, J., Gong, S., Xiang, T.: Head pose classification in crowded scenes. In: Proceedings of the British Machine Vision Conference, pp. 120.1–120.11. BMVA Press (2009). https://doi.org/10.5244/C.23.120
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 41.1–41.12. BMVA Press, September 2015. https://doi.org/10.5244/C.29.41
Patacchiola, M., Cangelosi, A.: Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods. Pattern Recogn. 71(Supplement C), 132–143 (2017). https://doi.org/10.1016/j.patcog.2017.06.009. http://www.sciencedirect.com/science/article/pii/S0031320317302327
Ranjan, R., Patel, V.M., Chellappa, R.: HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell., 1 (2018). https://doi.org/10.1109/TPAMI.2017.2781233
Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R.: An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp. 17–24, May 2017. https://doi.org/10.1109/FG.2017.137
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ruiz, N., Chong, E., Rehg, J.M.: Fine-grained head pose estimation without keypoints. CoRR abs/1710.00925 (2017). http://arxiv.org/abs/1710.00925
Setti, F., et al.: The s-hock dataset: a new benchmark for spectator crowd analysis. Comput. Vis. Image Underst. 159(Supplement C), 47–58 (2017). https://doi.org/10.1016/j.cviu.2017.01.003. Computer Vision in Sports. http://www.sciencedirect.com/science/article/pii/S1077314217300024
Shah, S., Ghosh, P., Davis, L.S., Goldstein, T.: Stacked U-nets: a no-frills approach to natural image segmentation. CoRR abs/1804.10343 (2018). http://arxiv.org/abs/1804.10343
Tosato, D., Spera, M., Cristani, M., Murino, V.: Characterizing humans on riemannian manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1972–1984 (2013). https://doi.org/10.1109/TPAMI.2012.263
Vu, T., Osokin, A., Laptev, I.: Context-aware CNNs for person head detection. In: International Conference on Computer Vision (ICCV) (2015)
Wang, C., Xu, C., Wang, C., Tao, D.: Perceptual adversarial networks for image-to-image transformation. IEEE Trans. Image Process. 27(8), 4066–4079 (2018). https://doi.org/10.1109/TIP.2018.2836316
Xu, X., Kakadiaris, I.A.: Joint head pose estimation and face alignment framework using global and local CNN features. In: 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp. 642–649, May 2017. https://doi.org/10.1109/FG.2017.81
Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 833–841, June 2015. https://doi.org/10.1109/CVPR.2015.7298684
Acknowledgment
This work was partially supported by an Australian Research Council grant DE120102960 and Murdoch University grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jan, Y., Sohel, F., Shiratuddin, M.F., Wong, K.W. (2019). WNet: Joint Multiple Head Detection and Head Pose Estimation from a Spectator Crowd Image. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-21074-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21073-1
Online ISBN: 978-3-030-21074-8
eBook Packages: Computer ScienceComputer Science (R0)