2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-2.12
Paper Title TRACKING VISUAL OBJECT AS AN EXTENDED TARGET
Authors Liang Xu, Ruixin Niu, Virginia Commonwealth University, United States
SessionMLR-APPL-IP-2: Machine learning for image processing 2
LocationArea E
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Most visual object tracking (VOT) algorithms treat the object as a single point in the output score map, and the bounding box is estimated by a multi-scale search. Further, most of them are based on the concept of tracking-by-detection, which is focused on the detection step and ignores the tracking step and object's dynamics. In this paper, we address these limitations by developing a new VOT framework. Instead of a point object, we mathematically model the shape of the extended visual object as an ellipse. We allow multiple detections in the score map, and derive an elliptical gating method to discard possible clutters. We apply a sophisticated extended target tracking algorithm to track the object's kinematic state and shape simultaneously. Experiment results are provided to show that the proposed algorithm outperforms several state-of-the-art methods.