caa a22 4500 605540578 CHVBK 20210128100912.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00371-014-1014-6 doi (NATIONALLICENCE)springer-10.1007/s00371-014-1014-6 Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis [Elektronische Daten] [Zhiyong Li, Shuang He, Mervat Hashem] Object tracking under complex circumstances is a challenging task because of background interference, obstacle occlusion, object deformation, etc. Given such conditions, robustly detecting, locating, and analyzing a target through single-feature representation are difficult tasks. Global features, such as color, are widely used in tracking, but may cause the object to drift under complex circumstances. Local features, such as HOG and SIFT, can precisely represent rigid targets, but these features lack the robustness of an object in motion. An effective method is adaptive fusion of multiple features in representing targets. The process of adaptively fusing different features is the key to robust object tracking. This study uses a multi-feature joint descriptor (MFJD) and the distance between joint histograms to measure the similarity between a target and its candidate patches. Color and HOG features are fused as the tracked object of the joint representation. This study also proposes a self-adaptive multi-feature fusion strategy that can adaptively adjust the joint weight of the fused features based on their stability and contrast measure scores. The mean shift process is adopted as the object tracking framework with multi-feature representation. The experimental results demonstrate that the proposed MFJD tracking method effectively handles background clutter, partial occlusion by obstacles, scale changes, and deformations. The novel method performs better than several state-of-the-art methods in real surveillance scenarios. Springer-Verlag Berlin Heidelberg, 2014 Object tracking nationallicence Feature fusion nationallicence Multi-feature joint descriptor nationallicence Stability nationallicence Contrast nationallicence Li Zhiyong College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut He Shuang College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut Hashem Mervat College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut The Visual Computer Springer Berlin Heidelberg 31/10(2015-10-01), 1319-1337 0178-2789 31:10<1319 2015 31 371 https://doi.org/10.1007/s00371-014-1014-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-1014-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Zhiyong College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut NATIONALLICENCE 700 1- He Shuang College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut NATIONALLICENCE 700 1- Hashem Mervat College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/10(2015-10-01), 1319-1337 0178-2789 31:10<1319 2015 31 371