@inproceedings {286,
	title = {Efficient target recovery using STAGE for mean-shift tracking},
	booktitle = {6th Canadian Conference on Computer and Robot Vision},
	year = {2009},
	month = {05/2009},
	abstract = {<p>Robust visual tracking is a challenging problem, especially when a target undergoes complete occlusion or leaves and later re-enters the camera view. The mean-shift tracker is an efficient appearance-based tracking algorithm that has become very popular in recent years. Many researchers have developed extensions to the algorithm that improve the appearance model used in target localization. We approach the problem from a slightly different angle and seek to improve the robustness of the mean-shift tracker by integrating an efficient failure recovery mechanism. The proposed method uses a novel application of the STAGE algorithm to efficiently recover a target in the event of tracking failure. The STAGE algorithm boosts the performance of a local search algorithm by iteratively learning an evaluation function to predict good states for initiating searches. STAGE can be viewed as a random-restart algorithm that chooses promising restart states based on the shape of the state space, as estimated using the search trajectories from previous iterations. In the proposed method, an adapted version of STAGE is applied to the mean-shift target localization algorithm (Bhattacharyya coefficient maximization using the mean-shift procedure) to efficiently recover the lost target. Experiments indicate that the proposed method is viable as a technique for recovering from failure caused by complete occlusion or departure from the camera view.</p>
},
	keywords = {appearance-based tracking algorithm, camera view departure, failure recovery mechanism, image processing, iterative learning, iterative methods, learning systems, local search algorithm, mean-shift target localization algorithm, mean-shift tracking, occlusion, random-restart algorithm, robust visual tracking, STAGE, target recovery, target tracking},
	doi = {http://dx.doi.org/10.1109/CRV.2009.23},
	author = {F Tung and J S. Zelek and D A. Clausi}
}
@inproceedings {370,
	title = {Toward an optimal solution for multitarget tracking},
	booktitle = {2007 IEEE International Conference on Image Processing},
	year = {2007},
	abstract = {<p>There are ever increasing number of applications of multi-target tracking and considerable research has been conducted to solve this problem. Multi-target tracking is a NP-hard problem and almost all of the present multi-target tracking algorithms are sub-optimal by finding the solution in a reduced hypothesis space. In this paper we introduce a new approach toward finding the optimal single frame solution for general multi-target tracking problem. Our proposed method finds the optimal solution using linear programming optimization method. The proposed method has been successfully applied to synthetic and real data.</p>
},
	keywords = {computational complexity, joint probabilistic data association, linear programming, multitarget tracking, NP-hard problem, optimal solution, optimization method, probability, sensor fusion, target tracking},
	doi = {http://dx.doi.org/10.1109/ICIP.2007.4379588},
	author = {N Nezamoddin Kachouie and P Fieguth}
}
