Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis

Verfasser / Beitragende:
[Zhiyong Li, Shuang He, Mervat Hashem]
Ort, Verlag, Jahr:
2015
Enthalten in:
The Visual Computer, 31/10(2015-10-01), 1319-1337
Format:
Artikel (online)
ID: 605540578
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024 7 0 |a 10.1007/s00371-014-1014-6  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00371-014-1014-6 
245 0 0 |a Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis  |h [Elektronische Daten]  |c [Zhiyong Li, Shuang He, Mervat Hashem] 
520 3 |a 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. 
540 |a Springer-Verlag Berlin Heidelberg, 2014 
690 7 |a Object tracking  |2 nationallicence 
690 7 |a Feature fusion  |2 nationallicence 
690 7 |a Multi-feature joint descriptor  |2 nationallicence 
690 7 |a Stability  |2 nationallicence 
690 7 |a Contrast  |2 nationallicence 
700 1 |a Li  |D Zhiyong  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
700 1 |a He  |D Shuang  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
700 1 |a Hashem  |D Mervat  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
773 0 |t The Visual Computer  |d Springer Berlin Heidelberg  |g 31/10(2015-10-01), 1319-1337  |x 0178-2789  |q 31:10<1319  |1 2015  |2 31  |o 371 
856 4 0 |u https://doi.org/10.1007/s00371-014-1014-6  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00371-014-1014-6  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Li  |D Zhiyong  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a He  |D Shuang  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Hashem  |D Mervat  |u College of Computer Science and Electronic Engineering, Hunan University, 410082, Changsha, China  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t The Visual Computer  |d Springer Berlin Heidelberg  |g 31/10(2015-10-01), 1319-1337  |x 0178-2789  |q 31:10<1319  |1 2015  |2 31  |o 371