Visual Tracking Using an Insect Vision Embedded Particle FilterReport as inadecuate

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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 573131, 16 pages -

Research Article

Beijing Key Laboratory of Intelligence Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China

School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK

Received 22 September 2014; Accepted 16 January 2015

Academic Editor: Ebrahim Momoniat

Copyright © 2015 Wei Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Particle filtering PF based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuronal computational model of the insect vision, we estimate the motion of the target in a novel way so as to refine the position state of propagated particles using more accurate transition mode. Furthermore, we design a novel sample optimization framework where local and global search strategies are jointly used. In addition, we propose a new method to monitor long duration severe occlusion and we could recover the target. Experiments on publicly available benchmark video sequences demonstrate that the proposed tracking algorithm outperforms the state-of-the art methods in challenging scenarios, especially for tracking target which is undergoing abrupt motion or fast movement.

Author: Wei Guo, Qingjie Zhao, and Dongbing Gu



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