A grid-based tracker for erratic targets. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Sang, Qian; Lin, Zongli; Acton, Scott T.
• Is Part Of: Pattern recognition. Volume 48:Issue 11(2015:Nov.)
• Page Start: 3527
• Page End: 3541
• Publication Date: 2015-11
• Subjects: Target tracking -- Visual tracking -- Grid-based Bayesian approach -- Erratic target motion -- Predication model adaptation -- Iterative position estimation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.05.014
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Highlights: The grid-based Bayesian tracker achieves excellent video tracking performance. We present a method to enhance its capability to track targets with erratic motion. We incorporate adaptation in the motion model and iterative position estimation. We present results on tracking of leukocytes, vehicles and Drosophila larva.