Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Sensors

Ming Xue¹  Hua Yang¹  Shibao Zheng¹  Yi Zhou² 

[1] Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; E-Mails:;Department of Electronics Engineering, Dalian Maritime University, Dalian 116026, China; E-Mail:

关键词: appearance model;    object tracking;    sparse representation;    structured dictionary learning;    Bayesian inference;    visual sensor networks;   

DOI  :  10.3390/s140203130

来源: mdpi

【 摘 要 】

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.