id: 05361692
dt: a
an: 05361692
au: Li, Yongwei; Hu, Shiqiang; Guo, Peng
ti: Neural network based visual tracking with multi-cue adaptive fusion.
so: Liu, Derong (ed.) et al., Advances in neural networks ‒ ISNN 2007. 4th
    international symposium on neural networks, ISNN 2007, Nanjing, China,
    June 3‒7, 2007. Proceedings, Part III. Berlin: Springer (ISBN
    978-3-540-72394-3/pbk). Lecture Notes in Computer Science 4493, 950-958
    (2007).
py: 2007
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-540-72395-0_116
ab: Summary: Visual tracking has been an active area of research in computer
    vision. However, robust tracking is still a challenging task due to
    cluttered backgrounds, occlusions and pose variations in the real
    world. To improve the tracking robustness, this paper proposes a
    tracking method based on multi-cue adaptive fusion. In this method,
    multiple cues, such as color and shape, are fused to represent the
    target observation. When fusing multiple cues, fuzzy logic is adopted
    to dynamically adjust each cue weight in the observation according to
    its associated reliability in the past frame. In searching and tracking
    object, neural network algorithm is applied, which improves the
    searching efficiency. Experimental results show that the proposed
    method is robust to illumination changes, pose variations, partial
    occlusions, cluttered backgrounds and camera motion.
rv: