Browse > Article
http://dx.doi.org/10.9708/jksci.2021.26.03.035

Deep learning based Person Re-identification with RGB-D sensors  

Kim, Min (Dept. of Industrial & Management Engineering, Inha University)
Park, Dong-Hyun (Dept. of Industrial & Management Engineering, Inha University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.3, 2021 , pp. 35-42 More about this Journal
Abstract
In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.
Keywords
person re-identification; surveillance system; deep learning; human action recognition; multi class identification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Collins, A. Lipton, T. Kanade, "A System for Video Surveillance and Monitoring", Proc. Am. Nuclear Soc. (ANS) Eighth Int'l Topical Meeting Robotic and Remote Systems, April, 1999.
2 B. Huang et al., "Sparsity-based occlusion handling method for person re-identification" in Multimedia Modeling, Cham, Switzerland:Springer, 2015.
3 S. Modi, S. Elliott, J. Whetsone, and H. Kim, "Impact of age groups on fingerprint recognition performance," in IEEE Workshop on Automatic Identification Advanced Technologies, pp. 19-23, 2007.
4 L. Deng, G. Hinton, B. Kingsbury, "New types of deep neural network learning for speech recognition and related applications: An overview", Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 8599-8603, 2013.
5 G. Johansson, "Visual Motion Perception", Scientific Am., vol. 232, pp. 76-88, 1975.   DOI
6 Z. Wu, Y. Li and R. J. Radke, "Viewpoint Invariant Human Re-Identification in Camera Networks Using Pose Priors and Subject-Discriminative Features," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, pp. 1095-1108, 1 May 2015, doi: 10.1109/TPAMI.2014.2360373.   DOI
7 Shu, Guang. "Human detection, tracking and segmentation in surveillance video." (2014).
8 S. Zhiyuan, M. Timothy, "Transferring a Semantic Representation for Person Re-Identification and Search," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4184-4193
9 T. Dutta, "Evaluation of the Kinect sensor for 3-D kinematic measurement in the workplace", Appl. Ergonom., vol. 43, no. 4, pp. 645-649, Jul. 2012.   DOI
10 Y. Huang, S. Luo, E. Chen, "An Efficient Iris Recognition System", Proc. First Int'l Conf. Machine Learning and Cybernetics, pp. 450-454, Nov 2002.
11 B. Bonnechère, B. Jansen, P. Salvia, H. Bouzahouene, L. Omelina, F. Moiseev, V. Sholukha, J. Cornelis, M. Rooze, S. Van Sint Jan, "Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogram metry", Gait & Posture, vol. 39, no. 1, pp. 593-598, 2014.   DOI
12 C. BenAbdelkader and R. Cutler, "View invariant estimation of height and stride for gait recognition", Workshop on Biometric Authentication ECCV, pp.15-167, May 2002.
13 M. S. Islam, and M. R. Islam, "Window based clothing invariant gait recognition", International Conference on Advances in Electrical Enginering(ICAEE), pp. 411-414, 2013
14 J. J. Litle and J. E. Boyd, "Recognition people by their gait: the shape of motion", Videre, vol. 1, no. 2, 1998.
15 L. Le, and W. E. L Grimson, "Gait apearance for recognition", Workshop on Biometric Authentication ECCV, pp. 143-154, 2002.
16 Q. -S. Li, Z. -T. Lu and D. -D. Zhang, "Integration of Gait and Side Face for Human Recognition in Video," 2009 Second International Symposium on Electronic Commerce and Security, Nanchang, 2009, pp. 65-69, doi: 10.1109/ISECS.2009.202.   DOI
17 A. Kale, A. K. R. Chowdhury, R. Chelapa, "Towards a view invariant gait recognition algorithm", Advanced video and signal based surveilance IEEE Conference on, pp. 143-150, July 2003.
18 Xiaxi Huang, N. V. Boulgouris, "Gait recognition with shifted energy image and structural feature extraction", Image Procesing, IEEE Transaction on, Vol. 21, pp. 256-268, April 2012.
19 D. Tan, K. Huang, S. Yu, and T. Tan, "Efficient night gait recognition based on template matching", The 18th International Conference on Patern Recognition(ICPR), vol. 3, pp. 100-103, August 2006.
20 Z. Xue, D. Ming, W. Song, B Wan, and S. Jin, "Infrared gait recognition based on wavelet transform and suport vector machine", Patern Recognition, vol. 43, no. 8, pp. 2904-2910, August 2010.   DOI
21 S.D. Khan and H. Ullah, "A Survey of Advances in Vision-based Vehicle Re-identification," Computer Vision and Image Understanding, Vol. 182, pp. 50-63, 2019.   DOI
22 Q. Leng, M. Ye, and Q. Tian, "A Survey of Open-world Person Re-identification," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, No. 4, pp. 1092-1108, 2019   DOI
23 E. Ahmed, M. Jones, and T. K. Marks, "An Improved Deep Learning Architecture for Person Re-Identification," Proceedings of the IEEE conference on computer vision and pattern recognition. pp.3908-3916, 2015.
24 L. Zheng, Y. Huang, H. Lu and Y. Yang, "Pose Invariant Embedding for Deep Person Re-Identification," IEEE Transactions on Image Processing, pp.4500-4509, 2019. DOI: 10.1109/TIP.2019.2910414   DOI
25 D. Wang, N. Canagarajah, D. Redmill and D. Bull, "Multiple description video coding based on zero padding," 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), Vancouver, BC, 2004, pp. II-205, doi: 10.1109/ISCAS.2004.1329244.   DOI
26 Manghisi, Vito Modesto, et al. "Real time RULA assessment using Kinect v2 sensor." Applied ergonomics 65 (2017): 481-491.   DOI
27 M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth and H. Bischof, "Large scale metric learning from equivalence constraints," 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2012, pp. 2288-2295, doi: 10.1109/CVPR.2012.6247939.   DOI
28 A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan and M. Shah, "Visual Tracking: An Experimental Survey," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 7, pp. 1442-1468, July 2014, doi: 10.11 09/TPAMI.2013.230.   DOI