Abstract
In this paper we propose a novel approach for video-based person re-identification that exploits convolutional neural networks to learn the similarity of persons observed from video camera. We take 3-dimensional convolutional neural networks (3D CNN) to extract fine-grained spatiotemporal features from the video sequence of a person. Unlike recurrent neural networks, 3D CNN preserves the spatial patterns of the input, which works well on re-identification problem. The network maps each video sequence of a person to a Euclidean space where distances between feature embeddings directly correspond to measures of person similarity. By our improved parameter learning method called entire triplet loss, all possible triplets in the mini-batch are taken into account to update network parameters. This parameter updating method significantly improves training, enabling the embeddings to be more discriminative. Experimental results show that our model achieves new state of the art identification rate on iLIDS-VID dataset and PRID-2011 dataset with 82.0%, 83.3% at rank 1, respectively.
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References
Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: CVPR (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)
Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: CVPR (2016)
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: CVPR (2015)
Gao, C., Wang, J., Liu, L., Yu, J.G., Sang, N.: Temporally aligned pooling representation for video-based person re-identification. In: ICIP (2016)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, C., Loy, C.C., Tang, X.: Local similarity-aware deep feature embedding. In: NIPS (2016)
Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: Deep filter pairing neural network for person re-identification. In: CVPR (2014)
Liu, K., Ma, B., Zhang, W., Huang, R.: A spatio-temporal appearance representation for viceo-based pedestrian re-identification. In: ICCV (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
McLaughlin, N., Del Rincon, J.M., Miller, P.: Data-augmentation for reducing dataset bias in person re-identification. In: AVSS (2015)
McLaughlin, N., Martinez del Rincon, J., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: CVPR (2016)
Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: CVPR (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS (2014)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: ICCV (2015)
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: CVPR (2014)
Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by discriminative selection in video ranking. In: PAMI (2016)
Wu, L., Shen, C., Hengel, A.v.d.: Deep recurrent convolutional networks for video-based person re-identification: An end-to-end approach. arXiv preprint arXiv:1606.01609 (2016)
Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: CVPR (2016)
You, J., Wu, A., Li, X., Zheng, W.S.: Top-push video-based person re-identification. In: CVPR (2016)
Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: ICCV (2013)
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: Mars: a video benchmark for large-scale person re-identification. In: ECCV (2016)
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Kato, N., Hakozaki, K., Tanabiki, M., Furuyama, J., Sato, Y., Aoki, Y. (2018). Video-Based Person Re-identification by 3D Convolutional Neural Networks and Improved Parameter Learning. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_18
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