Comparative Study on Person Re-identification Using Color and Shape Features-Based Body Part Matching
Researches on person re-identification have become more prevalent as a core technique in visual surveillance systems. While a large volume of person re-identification (Re-ID) methods adopt experimental settings with a large patch size, relatively small ones are not much considered. Thus, this work focuses on investigating comparison of person Re-ID performance when color and shape features-based are adopted for small patch size images with low resolution. First, the deep decompositional network is used to divide the person into upper and lower body parts. Then, color and shape features are extracted. Finally, using single or combination of features, similarity-based ranked matching scores are computed. The person Re-ID performance is evaluated based on VIPeR dataset. From the experiment results, we found that color-based feature is better than shape-based features, and the combination of color and shape features-based can be meaningful.
KeywordsPerson re-identification Deep decompositional network Body part matching Combination of features Color feature Shape feature
This work was supported by the ICT R&D program of MSIP/IITP [B0101-16-0525, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis], and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03036423) and the Brain Research Program through the NRF funded by the Ministry of Science, ICT & Future Planning (NRF-2016M3C7A1905477).
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