Abstract
State-of-the-art (STOA) person re-identification (re-ID) methods measure features extracted by deep CNNs for final evaluation. In this work, we aim to improve re-ID performance by better utilizing these deep features. Firstly, a Dynamic Re-ranking (DRR) method is proposed, which matches features based on neighborhood structure to utilize contextual information. Different from common re-ranking methods, it finds more matches by adding contextual information. Secondly, to exploit the diverse information embedded in the deep features, we introduce Deep Feature Fusion (DFF), which splits and combines deep features through a diffusion and fusion process. Extensive comparative evaluations on three large re-ID benchmarks and six well-known features show that DRR and DFF are effective and insensitive to parameter setting. With a proper integration strategy, DRR and DFF can achieve STOA re-ID performance.
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Acknowledgments
This work is supported by the Natural Science Foundation of China (No. 61672276) and the Natural Science Foundation of Jiangsu Province of China (No. BK20161406).
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Liu, Y., Shang, L., Song, A. (2019). Dynamic Re-ranking with Deep Features Fusion for Person Re-identification. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_16
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