Moving human tracking across multi-camera based on artificial immune random forest and improved colour-texture feature fusion

作者：Liu, Jian; Hao, Kuangrong^*; Ding, Yongsheng; Yang, Shiyu; Gao, Lei

来源：Imaging Science Journal, 2017, 65(4): 239-251.

DOI：10.1080/13682199.2017.1319608

• Non-overlapping
• human tracking
• local binary patterns(LBP)
• brightness transfer BTF)
• multi-camera

In multi-camera video tracking, the tracking scene and tracking-target appearance can become complex. To finish multi-camera tracking in these challenging, we first utilize an improved brightness transfer function to establish matching tasks of different cameras and to reduce the influence of brightness changes between multiple cameras. Then, we proposes an improved colour-texture feature fusion (ICTFF) that is composed of the colour features and texture features for multi-camera human tracking in non-overlapping field of view. It's the first time to use artificial immune random forest with fully exploit the linear combination information of the feature and colour feature that can achieving the optimization of the number of decision trees and the effective classification of the target feature. Compared with state-of-the-art algorithms, our ICTFF algorithm can significantly improve the tracking accuracy in some complex scenes, such as changing the speed, changing the direction and appearance of the target.

• 出版日期2017
• 单位CSIRO; 东华大学