Abstract
Person re-identification (Re-ID) aims to match persons across non-overlapping camera views at different time. Typical person Re-ID models include two critical components: feature representation and metric learning. Due to the large variations in a persons appearance by different poses, viewpoints, illumination and occlusions, metric learning is always a necessary part in person Re-ID. In this paper, we propose a Deep person Feature Representation (DFR) learning frame-work based on a classification-oriented convolution neural network, and the DFR is directly used to calculate cosine distance for the similarity measure while with-out explicit metric learning. In the framework, Batch Normalization (BN) is applied before the ReLU layer to accelerate the convergence process, and with dropout strategy the DFR is only 64-dimension which makes the feature representation more effective and less noisy. Experiments demonstrate that our approach achieves the state-of-the-art results on most of the challenging datasets, especially on dataset of the largest scale CUHK03.
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Acknowledgments
This work is supported by the Natural Science Foundation of China (NSFC) Grants 61301241, 61403353, 61303145, 61501417 and 61271405; Natural Science Foundation of Shandong (ZR2015FQ011; ZR2014FQ023); China Postdoctoral Science Foundation funded project (2016M590659); Qingdao Postdoctoral Science Foundation funded project(861605040008); The Fundamental Research Funds for the Central Universities (201511008, 30020084851).
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Wang, S., Duan, L., Zhao, Y., Dong, J. (2016). A Simple Deep Feature Representation for Person Re-identification. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_42
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DOI: https://doi.org/10.1007/978-981-10-3614-9_42
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