Browse > Article
http://dx.doi.org/10.6109/jkiice.2022.26.12.1816

Human Tracking System in Large Camera Networks using Face Information  

Lee, Younggun (Department of Electronics and Communication Engineering, Republic of Korea Air Force Academy)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.12, 2022 , pp. 1816-1825 More about this Journal
Abstract
In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.
Keywords
Human tracking; Camera network; Facial feature; Super-resolution; Dana36;
Citations & Related Records
연도 인용수 순위
  • Reference
1 V. S. Kenk, R. Mandeljc, S. Kovacic, M. Kristan, M. Hajdinjak, and J. Pers, "Visual re-identification across large, distributed camera networks," Image and Vision Computing, vol. 34, pp. 11-26, Feb. 2015.   DOI
2 T. Matsukawa, T. Okabe, E. Suzuki, and Y. Sato, "Hierarchical Gaussian Descriptors with Application to Person Re-Identification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 9, pp. 2179-2194, May 2019.   DOI
3 L. -C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation," in Proceedings of the European Conference on Computer Vision, Munich, Germany, pp. 801-818, 2018.
4 W. Chen, L. Cao, X. Chen, and K. Huang, "A Novel solution for multi-camera object tracking," in Proceedings of the IEEE International Conference on Image Processing, Paris, France, pp. 2329-2333, 2014.
5 Y. Cai and G. Medioni, "Exploring context information for inter-camera multiple target tracking," in Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs: CO, USA, pp. 761-768, 2014.
6 C. -T. Chu and J. -N. Hwang, "Fully Unsupervised Learning of Camera Link Models for Tracking Humans Across Nonoverlapping Cameras," IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 6, pp. 979-994, Jun. 2014.   DOI
7 Y. -G. Lee, Z. Tang, and J. -N. Hwang, "Online-Learning-Based Human Tracking Across Non-Overlapping Cameras," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2870-2883, May 2017.   DOI
8 H. -M. Hsu, T. -W. Huang, G. Wang, J. Cai, Z. Lei, and J. -N. Hwang, "Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach: CA, USA, pp. 416-424, 2019.
9 N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, and W. Wu, "Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology," in Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Korea, pp. 1457-1465, 2018.
10 M. Li, X. Zhu, and S. Gong, "Unsupervised Person Re-identification by Deep Learning Tracklet Association," in Proceedings of the European Conference on Computer Vision, Munich, Germany, pp. 737-753, 2018.
11 C. Ding, J. Choi, D. Tao, and L. S. Davis, "Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 3, pp. 518-531, Mar. 2016.   DOI
12 B. Chen, W. Deng, and J. Hu, "Mixed High-Order Attention Network for Person Re-Identification," in Proceedings of The IEEE/CVF International Conference on Computer Vision, Seoul, Korea, pp. 371-381, 2019.
13 E. -J. Cheng, K. -P. Chou, S. Rajora, B. -H. Jin, M. Tanveer, C. -T. Lin, K. -Y. Young, W. -C. Lin, and M. Prasad, "Deep Sparse Representation Classifier for facial recognition and detection system," Pattern Recognition Letters, vol. 125, pp. 71-77, Jul. 2019.   DOI
14 Y. -K. Li, X. -J. Wu, and J. Kittler, "L1-2D 2 PCANet: a deep learning network for face recognition," Journal of Electronic Imaging, vol. 28, no. 2, Mar. 2019.
15 S. Wu, M. Kan, Z. He, S. Shan, and X. Chen, "Funnel-structured cascade for Multi-view face detection with alignment-awareness," Neurocomputing, vol. 221, pp. 138-145, Jan. 2017.   DOI
16 J. Jiang, C. Chen, J. Ma, Z. Wang, Z. Wang, and R. Hu, "SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression With Local Structure Prior," IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 27-40, Aug. 2016.   DOI
17 Y. -G. Lee, S. -C. Chen, J. -N. Hwang, and Y. -P. Hung, "An Ensemble of Invariant Features for Person Reidentification," IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 3, pp. 470-483, Dec. 2016.   DOI
18 T. Matsukawa, T. Okabe, E. Suzuki, and Y. Sato, "Hierarchical Gaussian Descriptor for Person Re-identification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: NV, USA, pp. 1363-1372, 2016.
19 S. Sural, G. Qian, and S. Pramanik, "Segmentation and histogram generation using the HSV color space for image retrieval," in Proceedings of the International Conference on Image Processing, Rochester: NY, USA, pp. II-II, 2002.
20 M. S, M. Crocco, M. Cristani, S. Martelli, and V. Murino, "Heterogeneous Auto-similarities of Characteristics (HASC): Exploiting Relational Information for Classification," in Proceedings of the IEEE International Conference on Computer Vision, Sydney: NSW, Australia, pp. 809-816, 2013.
21 X. Xiong and F. D. Torre, "Supervised Descent Method and Its Applications to Face Alignment," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Portland: OR, USA, pp. 532-539, 2013.
22 E. Ristani and C. Tomasi, "Features for Multi-Target Multi-Camera Tracking and Re-Identification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City: UT, USA, pp. 6036-6046, 2018.
23 Y. He, X. Wei, X. Hong, W. Shi, and Y. Gong, "Multi-Target Multi-Camera Tracking by Tracklet-to-Target Assignment," IEEE Transactions on Image Processing, vol. 29, pp. 5191-5205, Mar. 2020.   DOI
24 O. Javed, K. Shafique, Z. Rasheed, and M. Shah, "Modeling intercamera space-time and appearance relationships for tracking across nonoverlapping views," Computer Vision and Image Understanding, vol. 109, no. 2, pp. 146-162, Feb. 2008.   DOI
25 K. Zhou, S. Hu, R. Zhu, and T. Wu, "Pedestrian re-ID based on deep feature," in Proceedings of the IEEE CSAA Guidance, Navigation and Control Conference, Xiamen, China, pp. 1-5, 2018.
26 Y. Duan, J. Lu, and J. Zhou, "UniformFace: Learning Deep Equidistributed Representation for Face Recognition," in Proceedings of the IEEE/CVF Conf. Computer Vision Pattern Recognition, Long Beach: CA, USA, pp. 3415-3424, 2019.
27 M. Faraki, M. T. Harandi, and F. Porikli, "More about VLAD: A leap from Euclidean to Riemannian manifolds," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston: MA, USA, pp. 4951-4960, 2015.
28 T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Aug. 2002.   DOI