Fusing Appearance Statistical Features for Person Re-identification
Zeng Ming-yong① Wu Ze-min①② Tian Chang① Fu Yi① Jie Fei-ran②
①(College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China) ②(Science and Technology on Electro-Optic Control Laboratory, Aviation Industry Corporation of China, Luoyang 417009, China)
Abstract：Person re-identification is among the key issues in video surveillance. From the viewpoint of fusing appearance statistical features, human color and structure information are exploited; two statistical descriptors named spatiogram and region covariance are both explored on feature designing and metric choosing. Several complimentary feature vectors are extracted from a proper number of hierarchical image layers and regions. The simplest l1 norm distance is chosen to form the proposed weighted combining distance. The fused method with such two descriptors requires neither preprocessing nor supervised training. Extensive experiments by comparisons and analysis show that the proposed method not only achieves the state-of-the-art re-identification performance, but also enjoys a great applicability.
2014, Vol. 36 
