Abrupt motion tracking, a challenge, solution and its applications
Speaker: Professor Yao Lu, Director of Research Centre for Intelligent Computation, Beijing Institute of Technology (BIT), China.
Date and Time: 1:30 – 2:30pm, 18 December, 2014
Seminar Room: CB11.04.401, Broadway FEIT Building 11
Seminar Chairman: Associate Professor Jian Zhang (jian.zhang@uts.edu.au)
Abstract: Complex Visual Tracking is an active research area in computer vision since it faces to some challenge problems which stimulate interesting of scientist, and also has many important applications, such as tracking of objects such as people, animal, vehicle, and the others, even including complex video events analysis. There are two main difficulties to overcome in this task: abrupt motion and non-rigidness of the tracked object. For the first problem, we have presented a novel solution, IASAMC, for the first time. IASAMC means Intensively Adaptive Stochastic Approach Mento Carlo sampling for tracking of abrupt motion, which can sample from the complicated filtering distribution. In sampling, the history samples of the simulated Markov chain are exploited to establish a density grid based predictive model, and the “optimal” transition kernel is adaptively learned by using this model to improve the mixing of MCMC sampling. More precisely, the proposed algorithm works based on two collaborative online learning processes, of which one aims to approximate to the target distribution by estimating the density of states (DoS), and another one attempts to estimate the optimal proposal distribution based on the density grid based predictive model. The proposed algorithm can effectively overcome the local-trap problem in sampling by random walk in the energy space, as well as speed up the convergence rate due to its adaptive sampling mechanism. Extensive experiments show that the proposed tracking algorithm works effectively and outperforms several state-of-the-art alternatives in tracking of various types of abrupt motions, including sudden dynamics changes, camera switching, low-frame-rate videos, and etc.
Short CV of Yao Lu:
A Professor, Beijing Institute of Technology(BIT), School of Computer Science and Technology, Beijing, China. Director of Research Center for Intelligent Computation. He was an Invited Professor, Engineering Faculty, Gunma University, Kiryu, Japan, July 1, 2003 to June 30, 2004. His research interests include image/video processing, object detecting and tracking, face hallucination, video event analysis, neural network, and etc. He has published more than 60 peer-reviewed conference or journal papers and obtained 3 patents in recent ten years. He has undertaken and completed several important scientific research projects supported by Natural Science Foundation of China(NSFC), Beijing Natural Science Foundation(BNSF), and Education Ministry of China(EMC). Especially, he has lead some large projects supported from the Academy of CASIC. He has gained Excellent Novelty Award of BIT, CASIC. He is a member of review committee of NSFC, BJNSF, EMC and of Science and Technology Award Evaluation Committee of China.