CNN-based Occluded Person Re-identification in a Multi Camera Environment
Main Article Content
Keywords
Person re-identification, deep learning, ensemble deep learning
Abstract
In the context of rising global urban security concerns and the growing use of surveillance cameras, this study aims to enhance individual identification accuracy in occlusion scenarios using deep learning. Four CNN-based models for person re-identification are analyzed and put into practice. Additionally, comparative studies are conducted, and the model’s performance is assessed using the Market-1501 and Occluded-Reid datasets. We propose the use of ensemble learning and convolutional neural networks (CNNs) to address occlusion issues. Our results show that the ensemble approach performs better in re-identification tasks than traditional deep learning algorithms with an improvement of 1%–2% in mAP and Rank-1 scores, respectively.
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References
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Gray, D., Brennan, S., & Tao, H. (2007). Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. https://api.semanticscholar.org/CorpusID:15225312
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385
He, L., & Liu, W. (2020). Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes. In Vedaldi, A., Bischof, H., Brox, T., Frahm, J. M. (eds), Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol. 12373. Springer, Cham. https://doi.org/10.1007/978-3-030-58604-1_22
He, L., Wang, Y., Liu, W., Zhao, H., Sun, Z., & Feng, J. (2019). Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 8449–8458. https://doi.org/10.1109/ICCV.2019.00854
Hermans, A., Beyer, L., & Leibe, B. (2017). In Defense of the Triplet Loss for Person Re-Identification. http://arxiv.org/abs/1703.07737
Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. http://arxiv.org/abs/1608.06993
Karahan, S., Yildirim, M. K., Kirtac, K., Rende, F. S., Butun, G., & Ekenel, H. K. (2016). How Image Degradations Affect Deep CNN-based Face Recognition? 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2016, pp. 1–5. https://doi.org/10.1109/BIOSIG.2016.7736924
Kaya, M., & Bilge, H. Ş. (2019). Deep metric learning: A survey. Symmetry, 11(9), 1066. https://doi.org/10.3390/sym11091066
Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. http://arxiv.org/abs/1412.6980
Li, M., Zhu, X., & Gong, S. (2018). Unsupervised Person Re-identification by Deep Learning Tracklet Association. http://arxiv.org/abs/1809.02874
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014). DeepReID: Deep filter pairing neural network for person re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 152–159. https://doi.org/10.1109/CVPR.2014.27
Li, W., Zhu, X., & Gong, S. (2018). Harmonious Attention Network for Person Re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2285–2294. https://doi.org/10.1109/CVPR.2018.00243
Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., & Wu, F. (2021). Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 2897–2906 https://doi.org/10.1109/CVPR46437.2021.00292
Li, Z., Han, Z., Xing, J., Ye, Q., Yu, X., & Jiao, J. (2019). High performance person re-identification via a boosting ranking ensemble. Pattern Recognition, 94, 187–195. https://doi.org/10.1016/j.patcog.2019.05.022
Mauldin, T. A., Ngu, A., Metsis, V., Canby, M. E., & Tesic, J. (2019). Experimentation and Analysis of Ensemble Deep Learning in IoT Applications. Open Journal of Internet of Things (OJIOT), 5(1), 133–149. https://api.semanticscholar.org/CorpusID:264249126
Ming, Z., Zhu, M., Wang, X., Zhu, J., Cheng, J., Gao, C., Yang, Y., & Wei, X. (2022). Deep learning-based person re-identification methods: A survey and outlook of recent works. Image and Vision Computing, 119. https://doi.org/10.1016/j.imavis.2022.104394
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 1846–1855. https://doi.org/10.1109/CVPR.2015.7298794
Perin, G., Chmielewski, Ł., & Picek, S. (2020). Strength in numbers: Improving generalization with ensembles in machine learning-based profiled side-channel analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2020(4), 337–364. https://doi.org/10.13154/tches.v2020.i4.337-364
Rajaraman, S., Jaeger, S., & Antani, S. K. (2019). Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ, 7. https://doi.org/10.7717/PEERJ.6977
Ristani, E., Solera, F., Zou, R. S., Cucchiara, R., & Tomasi, C. (2016). Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. http://arxiv.org/abs/1609.01775
Serbetci, A., & Akgul, Y. S. (2020). End-to-end training of CNN ensembles for person re-identification. Pattern Recognition, 104, 107319. https://doi.org/10.1016/j.patcog.2020.107319
Shim, K., Yoon, S., Ko, K., & Kim, C. (2021). Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2021, pp. 4188–4195. https://doi.org/10.1109/CVPRW53098.2021.00473
Singh, S., Hoiem, D., & Forsyth, D. (2016). Swapout: Learning an ensemble of deep architectures. http://arxiv.org/abs/1605.06465
Srivastava, N., Hinton, G., Krizhevsky, A., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56), 1929–1958. http://jmlr.org/papers/v15/srivastava14a.html
Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., & Sun, J. (2020). High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification. https://arxiv.org/abs/2003.08177
Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. Y. (2017). Generative adversarial networks: Introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4), 588–598. https://doi.org/10.1109/JAS.2017.7510583
Wang, K., Wang, H., Liu, M., Xing, X., & Han, T. (2018). Survey on person re-identification based on deep learning. CAAI Transactions on Intelligence Technology, 3(4), 219–227. https://doi.org/10.1049/trit.2018.1001
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2017). Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. http://arxiv.org/abs/1711.08565
Wei, W., Yang, W., Zuo, E., Qian, Y., & Wang, L. (2022). Person re-identification based on deep learning — An overview. Journal of Visual Communication and Image Representation, 82, 103418. https://doi.org/10.1016/j.jvcir.2021.103418
Wu, Y., Lin, Y., Dong, X., Ya, Y., Ouyang, W., & Yang, Y. (2018). Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 5177–5186. https://doi.org/10.1109/CVPR.2018.00543
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). Aggregated Residual Transformations for Deep Neural Networks. http://arxiv.org/abs/1611.05431
Yan, C., Pang, G., Jiao, J., Bai, X., Feng, X., & Shen, C. (2021). Occluded Person Re-Identification with Single-scale Global Representations. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 11855–11864. https://doi.org/10.1109/ICCV48922.2021.01166
Yang, Y., Liu, X., Ye, Q., & Tao, D. (2018). Ensemble learning-based person re-identification with multiple feature representations. Complexity, 2018, 5940181. https://doi.org/10.1155/2018/5940181
Zahra, A., Perwaiz, N., Shahzad, M., & Fraz, M. M. (2022). Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends. http://arxiv.org/abs/2202.13121
Zhang, T., Xie, L., Wei, L., Zhang, Y., Li, B., & Tian, Q. (2019). Single Camera Training for Person Re-identification. http://arxiv.org/abs/1909.10848
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable Person Re-identification: A Benchmark. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1116–1124 https://doi.org/10.1109/ICCV.2015.133
Zhou, K., Yang, Y., Cavallaro, A., & Xiang, T. (2019). Omni-Scale Feature Learning for Person Re-Identification. http://arxiv.org/abs/1905.00953
Zhuo, J., Chen, Z., Lai, J., & Wang, G. (2018). Occluded Person Re-identification. http://arxiv.org/abs/1804.02792
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., & Wilson, A. G. (2018). Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. http://arxiv.org/abs/1802.10026
Gray, D., Brennan, S., & Tao, H. (2007). Evaluating Appearance Models for Recognition, Reacquisition, and Tracking. https://api.semanticscholar.org/CorpusID:15225312
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. http://arxiv.org/abs/1512.03385
He, L., & Liu, W. (2020). Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes. In Vedaldi, A., Bischof, H., Brox, T., Frahm, J. M. (eds), Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol. 12373. Springer, Cham. https://doi.org/10.1007/978-3-030-58604-1_22
He, L., Wang, Y., Liu, W., Zhao, H., Sun, Z., & Feng, J. (2019). Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 8449–8458. https://doi.org/10.1109/ICCV.2019.00854
Hermans, A., Beyer, L., & Leibe, B. (2017). In Defense of the Triplet Loss for Person Re-Identification. http://arxiv.org/abs/1703.07737
Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2016). Densely Connected Convolutional Networks. http://arxiv.org/abs/1608.06993
Karahan, S., Yildirim, M. K., Kirtac, K., Rende, F. S., Butun, G., & Ekenel, H. K. (2016). How Image Degradations Affect Deep CNN-based Face Recognition? 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2016, pp. 1–5. https://doi.org/10.1109/BIOSIG.2016.7736924
Kaya, M., & Bilge, H. Ş. (2019). Deep metric learning: A survey. Symmetry, 11(9), 1066. https://doi.org/10.3390/sym11091066
Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization. http://arxiv.org/abs/1412.6980
Li, M., Zhu, X., & Gong, S. (2018). Unsupervised Person Re-identification by Deep Learning Tracklet Association. http://arxiv.org/abs/1809.02874
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014). DeepReID: Deep filter pairing neural network for person re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 152–159. https://doi.org/10.1109/CVPR.2014.27
Li, W., Zhu, X., & Gong, S. (2018). Harmonious Attention Network for Person Re-identification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2285–2294. https://doi.org/10.1109/CVPR.2018.00243
Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., & Wu, F. (2021). Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 2897–2906 https://doi.org/10.1109/CVPR46437.2021.00292
Li, Z., Han, Z., Xing, J., Ye, Q., Yu, X., & Jiao, J. (2019). High performance person re-identification via a boosting ranking ensemble. Pattern Recognition, 94, 187–195. https://doi.org/10.1016/j.patcog.2019.05.022
Mauldin, T. A., Ngu, A., Metsis, V., Canby, M. E., & Tesic, J. (2019). Experimentation and Analysis of Ensemble Deep Learning in IoT Applications. Open Journal of Internet of Things (OJIOT), 5(1), 133–149. https://api.semanticscholar.org/CorpusID:264249126
Ming, Z., Zhu, M., Wang, X., Zhu, J., Cheng, J., Gao, C., Yang, Y., & Wei, X. (2022). Deep learning-based person re-identification methods: A survey and outlook of recent works. Image and Vision Computing, 119. https://doi.org/10.1016/j.imavis.2022.104394
Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 1846–1855. https://doi.org/10.1109/CVPR.2015.7298794
Perin, G., Chmielewski, Ł., & Picek, S. (2020). Strength in numbers: Improving generalization with ensembles in machine learning-based profiled side-channel analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2020(4), 337–364. https://doi.org/10.13154/tches.v2020.i4.337-364
Rajaraman, S., Jaeger, S., & Antani, S. K. (2019). Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ, 7. https://doi.org/10.7717/PEERJ.6977
Ristani, E., Solera, F., Zou, R. S., Cucchiara, R., & Tomasi, C. (2016). Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. http://arxiv.org/abs/1609.01775
Serbetci, A., & Akgul, Y. S. (2020). End-to-end training of CNN ensembles for person re-identification. Pattern Recognition, 104, 107319. https://doi.org/10.1016/j.patcog.2020.107319
Shim, K., Yoon, S., Ko, K., & Kim, C. (2021). Multi-Target Multi-Camera Vehicle Tracking for City-Scale Traffic Management. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2021, pp. 4188–4195. https://doi.org/10.1109/CVPRW53098.2021.00473
Singh, S., Hoiem, D., & Forsyth, D. (2016). Swapout: Learning an ensemble of deep architectures. http://arxiv.org/abs/1605.06465
Srivastava, N., Hinton, G., Krizhevsky, A., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56), 1929–1958. http://jmlr.org/papers/v15/srivastava14a.html
Wang, G., Yang, S., Liu, H., Wang, Z., Yang, Y., Wang, S., Yu, G., Zhou, E., & Sun, J. (2020). High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification. https://arxiv.org/abs/2003.08177
Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. Y. (2017). Generative adversarial networks: Introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4), 588–598. https://doi.org/10.1109/JAS.2017.7510583
Wang, K., Wang, H., Liu, M., Xing, X., & Han, T. (2018). Survey on person re-identification based on deep learning. CAAI Transactions on Intelligence Technology, 3(4), 219–227. https://doi.org/10.1049/trit.2018.1001
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2017). Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. http://arxiv.org/abs/1711.08565
Wei, W., Yang, W., Zuo, E., Qian, Y., & Wang, L. (2022). Person re-identification based on deep learning — An overview. Journal of Visual Communication and Image Representation, 82, 103418. https://doi.org/10.1016/j.jvcir.2021.103418
Wu, Y., Lin, Y., Dong, X., Ya, Y., Ouyang, W., & Yang, Y. (2018). Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 5177–5186. https://doi.org/10.1109/CVPR.2018.00543
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). Aggregated Residual Transformations for Deep Neural Networks. http://arxiv.org/abs/1611.05431
Yan, C., Pang, G., Jiao, J., Bai, X., Feng, X., & Shen, C. (2021). Occluded Person Re-Identification with Single-scale Global Representations. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 11855–11864. https://doi.org/10.1109/ICCV48922.2021.01166
Yang, Y., Liu, X., Ye, Q., & Tao, D. (2018). Ensemble learning-based person re-identification with multiple feature representations. Complexity, 2018, 5940181. https://doi.org/10.1155/2018/5940181
Zahra, A., Perwaiz, N., Shahzad, M., & Fraz, M. M. (2022). Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends. http://arxiv.org/abs/2202.13121
Zhang, T., Xie, L., Wei, L., Zhang, Y., Li, B., & Tian, Q. (2019). Single Camera Training for Person Re-identification. http://arxiv.org/abs/1909.10848
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable Person Re-identification: A Benchmark. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp. 1116–1124 https://doi.org/10.1109/ICCV.2015.133
Zhou, K., Yang, Y., Cavallaro, A., & Xiang, T. (2019). Omni-Scale Feature Learning for Person Re-Identification. http://arxiv.org/abs/1905.00953
Zhuo, J., Chen, Z., Lai, J., & Wang, G. (2018). Occluded Person Re-identification. http://arxiv.org/abs/1804.02792
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Published
Dec 18, 2023
Article Details
How to Cite
Shahrin, A. I. B., & Hashim, T. D. N. B. (2023). CNN-based Occluded Person Re-identification in a Multi Camera Environment. Journal of Telecommunications and the Digital Economy, 11(4), 113–130. https://doi.org/10.18080/jtde.v11n4.773
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Telecommunications
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