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Fast and Robust Object Tracking with Adaptive Detection | IEEE Conference Publication | IEEE Xplore

Fast and Robust Object Tracking with Adaptive Detection


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...Show More

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 also demonstrates that our approach greatly outperforms the state-of-the-art methods in terms of tracking speed and accuracy.
Date of Conference: 06-08 November 2016
Date Added to IEEE Xplore: 16 January 2017
ISBN Information:
Electronic ISSN: 2375-0197
Conference Location: San Jose, CA, USA

References

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