A grid-based tracker for erratic targets

被引:3
|
作者
Sang, Qian [1 ]
Lin, Zongli [2 ]
Acton, Scott T. [2 ]
机构
[1] Aol Platforms R&D, Algorithm Res, Palo Alto, CA 94306 USA
[2] Univ Virginia, Charles L Brown Dept Elect & Comp Engn, Charlottesville, VA 22904 USA
关键词
Target tracking; Visual tracking; Grid-based Bayesian approach; Erratic target motion; Predication model adaptation; Iterative position estimation; SEQUENTIAL MONTE-CARLO; BAYESIAN-APPROACH; PARTICLE FILTERS; VISUAL TRACKING; IN-VIVO; TUTORIAL;
D O I
10.1016/j.patcog.2015.05.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The grid-based Bayesian tracker employs a novel sample generation and weighting mechanism that achieves significantly improved visual tracking performance (in terms of accuracy, robustness, and computational burden) over existing active contour trackers and Monte Carlo trackers. This paper presents a method to enhance its capability in accommodating the tracking of targets in video with erratic motion, by introducing adaptation in the motion model and iterative position estimation. Tracking performance of the resulting algorithm is compared with the grid-based Bayesian tracker in the context of leukocyte tracking, UAV-based vehicle tracking, and Drosophila larva tracking to demonstrate its effectiveness in dealing with erratic target movement. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3527 / 3541
页数:15
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