Abstract

Object detection and tracking is an important research topic in computer vision with numerous practical applications. Although great progress has been made both in object detection and tracking, it is still a big challenge in automatic real-time applications. In this paper, a fast and robust approach is proposed by integrating an adaptive object detection technique within a kernelized correlation filter (KCF) framework. The KCF tracker is automatically initialized via salient object detection and localization. An adaptive object detection strategy is proposed to refine the location and boundary of the object when the tracking confidence value is below a certain threshold. In addition, a reliable post-processing technique is designed to accurately localize the object from a saliency map. Extensive quantitative and qualitative experiments on the challenging datasets have been performed to verify the proposed approach, which …

Date

2016

Authors

Sushil Pratap Bharati, Soumyaroop Nandi, Yuanwei Wu, Yao Sui, Guanghui Wang

Conference

2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI)

Pages

706-713

Publisher

IEEE