Abstract
Automated surveillance systems are required for the state of the art security. Everyday networks of cameras generate a very-large set of data, which makes recognition and identification tasks harder. In this paper, we present a new problem called Gait Retrieval in order to address the challenge of large-scale surveillance data. We have an interest in retrieving similar videos based on the human gait. Gait is the most important biometric for long distance human identification. We also propose a solution for the Gait Retrieval problem by using gait biometrics. The solution is based on the deep hashing technique to learn a hash function that preserves the similarities between the same labeled images. Deep hash function with convolutional neural network learns features and maps them to hash codes. Images with similar appearance should have similar hash codes. Training samples are arranged in a batch of triplets. Our proposed method outperforms traditional methods with good margin.
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
Vezzani, R., Baltieri, D., Cucchiara, R.: People re-identification in surveillance and forensics: a survey. ACM Comput. Surv. (CSUR) 46(2), 29 (2013)
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and and Pattern Recognition (CVPR), pp. 3586–3593. IEEE (2013)
Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 144–151 (2014)
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, pp. 262–275, October 2008
Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Under Standing 117(2), 130–144 (2013)
Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for personre-identification. In: European Conference on Computer Vision Workshops, pp. 413–422, October 2012
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 209–216, June 2007
Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473–1480 (2005)
Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith J.R.: Learning locally-adaptive decision functions for person verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3610–3617 (2013)
Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recogn. Lett. 13(13), 2052–2060 (2010)
Xu, D., Huang, Y., Zeng, Z., Xu, X.: Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans. Image Process. 21(1), 316–326 (2012)
Hu, M., Wang, Y., Zhang, Z., Zhang, D., Little, J.J.: Incremental learning for video-based gait recognition with LBP flow. IEEE Trans. Cybern. 43(1), 77–89 (2013)
Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proceeding 5th IEEE Internationa Conference AFGR, pp. 148–155, May 2002
Bhanur, B., Han, J.: Human Recognition at a Distance in Video. Springer Science and Business Media, Berlin (2011)
Gu, J., Ding, X., Wang, S., Wu, Y.: Action and gait recognition from recovered 3-D human joints. IEEE Trans. Syst. Man Cybern. 40(4), 1021–1033 (2010)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)
Fang, Z., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Cao, Y., Xu, J., Liu, T., Li, H., Huang, Y., Hon, H.: Adapting ranking SVM to document retrieval. In: Proceeding Annual ACM SIGIR Conference, pp.186–193, August 2006
Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, March 2014
Zheng, L., Zhang, Z., Wu, Q., Wang, Y.: Enhancing person re-identification by integrating gait biometric. Neurocomputing 168, 1144–1156 (2015)
Huang, S., Elgammal, A., Lu, J., Yang, D.: Cross-speed gait recognition using speed-invariant gait templates and globality locality preserving projections. IEEE Trans. Inf. Forensics Secur. 10(10), 2071–2083 (2015)
Li, X., Lin, G., Shen, C., van den Hengel, A., Dick, A.R.: Learning hash functions using column generation, arXiv preprint arXiv, March 2013
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process 11(4), 467–476 (2002)
Jarvelin, K., Kekalainen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2000)
Acknowledgmets
This work is jointly supported by National Basic Research Program of China (2012CB316300), National Natural Science Foundation of China (61420106015, 61525306). The authors would also like to acknowledge the support by Strategic Perority Research Program of the CAS (Grant XDB02070001), Youth Innovation Promotion Association CAS (200612), and SAMSUNG GRO Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rauf, M., Huang, Y., Wang, L. (2016). Gait Retrieval: A Deep Hashing Method for People Retrieval in Video. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_32
Download citation
DOI: https://doi.org/10.1007/978-981-10-3002-4_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3001-7
Online ISBN: 978-981-10-3002-4
eBook Packages: Computer ScienceComputer Science (R0)