Tracking three-dimensional moving light displays
Proc. of the ACM SIGGRAPH/SIGART interdisciplinary workshop on Motion: representation and perception
Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence
CVGIP: Image Understanding
A review of statistical data association for motion correspondence
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Video object tracking using adaptive Kalman filter
Journal of Visual Communication and Image Representation
A real-time object detecting and tracking system for outdoor night surveillance
Pattern Recognition
Modern Applied Statistics with S
Modern Applied Statistics with S
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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In this paper an efficient and applicable approach for tracking multiple similar objects in dynamic environments is proposed. Objects are detected based on a specific color pattern i.e. color label. It is assumed that the number of objects is not fixed and they can be occluded by other objects. Considering the detected objects, an efficient algorithm to solve the multi-frame object correspondence problem is presented. The proposed algorithm is divided into two steps; at the first step, previous mismatched correspondences are corrected using the new information (i.e. new detected objects in new image frame), then all tail objects (i.e. objects which are located at the end of a track) are tried to be matched with unmatched objects (either a new object or a previously mismatched object). Apart from the correspondence algorithm, a probabilistic gain function is used to specify the matching weight between objects in consecutive frames. This gain function benefits Student T distribution function for comparing different object feature vectors. The result of the algorithm on real data shows the efficiency and reliability of the proposed method.