Improving Retrieval Efficiency of Person Re-Identification Based on Resnet50
Pages 98 - 102
Abstract
In recent years, the issue of person re-identification has become more and more popular, which is an important research subject in the field of computer vision, and many models or methods for different predicaments have been proposed successively. However, there are often differences between theory and practice. As a matter of fact, while collecting a large number of pedestrian images, retrieval efficiency becomes one of the significant evaluation indicators. Therefore, how to maintain high precision and quickly respond to retrieval requirements is a very important issue. This thesis explores many proposed person re-identification methods and improves retrieval time under the premise of maintaining a high precision rate. In this paper, we select Resnet50 as the feature output model, and use not only K-means Clustering to filter out the preliminary candidates but also Hierarchical Comparison to reduce the number of feature comparisons. The final experimental result shows the average retrieval time is improved dramatically with a speed-up ratio closed to 8, whereas the precision loss is under 3%.
References
[1]
Cho, Y., and Yoon, K. 2018. PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification. IEEE T IMAGE PROCESS, vol.27 (March 2018), 3739--3752. DOI= https://doi.org/10.1109/TIP.2018.2815840.
[2]
Fu, M., Sun, S., Chen, N., Wang, D., and Tong, X. 2019. Deep Fusion Feature Presentations for Nonaligned Person Re-identification. IEEE ACCESS, vol.7 (June 2019), 73253--73261. DOI= https://doi.org/10.1109/ACCESS.2019.2920426.
[3]
Hao, L., Wang, D., and Zhu, Y. 2017. Learning biased distance metrics with diversity regularizer for person reidentification. In IEEE Visual Communications and Image Processing (Dec. 10-13, 2017). DOI= https://doi.org/10.1109/VCIP.2017.8305125.
[4]
Ma, F., Jing, X., Zhu, X., Tang, Z., and Peng, Z. 2019. True-Color and Grayscale Video Person Re-Identification. IEEE T INF FOREN SEC, vol.15 (May 2019). DOI= https://doi.org/10.1109/TIFS.2019.2917160.
[5]
Sun, L., Zhou, Y., Liu, J., Jiang, Z., and Yang, Z. 2018. Graph Regularized and Label-matched Dictionary Learning for Video-based Person Re-identification. In IEEE Visual Communications and Image Processing (Dec. 9-12, 2018). DOI= https://doi.org/10.1109/VCIP.2018.8698671.
[6]
Sun, J., and Jung, C. 2019. Unsupervised Person Re-identification Using Reliable and Soft Labels. In IEEE International Conference on Acoustics, Speech and Signal Processing (May 12-17, 2019). DOI= https://doi.org/10.1109/ICASSP.2019.8683353.
[7]
Tan, F., Zhao, X., Liu, K., and Liao Q. 2018. Person Re-Identification across Non-Overlapping Cameras Based on Two-Stage Framework. In 10th International Conference on Measuring Technology and Mechatronics Automation (Feb. 10-11, 2018). DOI= https://doi.org/10.1109/ICMTMA.2018.00063.
[8]
Wang, S., and Chen, Y. 2019. Dual Attentive Features for Person Re-identification. In 5th International Conference on Control, Automation and Robotics (April 19-22, 2019). DOI= https://doi.org/10.1109/ICCAR.2019.8813500.
[9]
Wang, G., Zhang, T., Yang, Y., Cheng, J., Chang, J., Liang, X., and Hou, Z. 2020. Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification. In AAAI-20 (Feb. 7-12, 2020).
[10]
Yang, W., Yan, Y., and Chen, S. 2020. Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions. Neurocomputing, Volume 340 (May 7, 2019), 125--132. DOI= https://doi.org/10.1016/j.neucom.2019.02.042.
[11]
Yang, Q., Wu, A., and Zheng, W. 2019. Person Re-identification by Contour Sketch under Moderate Clothing Change. IEEE TPAMI (Dec. 18, 2019). DOI= https://doi.org/10.1109/TPAMI.2019.2960509.
[12]
Zhang, J, Yuan, Y., and Wang, Q. 2019. Night Person Re-Identification and a Benchmark. IEEE ACCESS, vol. 7 (July 2019), 2169--3536. DOI= https://doi.org/10.1109/ACCESS.2019.2929854.
[13]
Zhao, H., Han, Z., Li, Z., and Qin, F. 2017. Unsupervised person re-identification via re-ranking enhanced sample-specific metric learning. In IEEE International Conference on Image Processing (Sept. 17-20, 2017). DOI= https://doi.org/10.1109/ICIP.2017.8296914.
[14]
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. 2015. Scalable Person Re-identification: A Benchmark. In IEEE ICCV (Dec. 7-13, 2015), 1116--1124. DOI= https://doi.org/10.1109/ICCV.2015.133.
[15]
Zhong, Z., Zheng, L., Cao, D., and Li, S. 2017. Re-ranking Person Re-identification with k-reciprocal Encoding. In PROC CVPR IEEE (July 21-26, 2017). DOI= https://doi.org/10.1109/CVPR.2017.389.
[16]
Zhuo, J., Chen, Z., Lai, J., and Wang, G. 2018. Occluded Person Re-Identification. In IEEE International Conference on Multimedia and Expo (July 23-27, 2018). DOI= https://doi.org/10.1109/ICME.2018.8486568.
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- Improving Retrieval Efficiency of Person Re-Identification Based on Resnet50
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Published In
June 2020
235 pages
ISBN:9781450375337
DOI:10.1145/3404709
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Association for Computing Machinery
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Publication History
Published: 13 July 2020
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ICFET 2020
ICFET 2020: 2020 The 6th International Conference on Frontiers of Educational Technologies
June 5 - 8, 2020
Tokyo, Japan
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