A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
Robust model-based motion tracking through the integration of search and estimation
International Journal of Computer Vision
Active vision
Dynamic contours: real-time active splines
Active vision
A Bayesian multiple-hypothesis approach to edge grouping and contour segmentation
International Journal of Computer Vision
A review of statistical data association for motion correspondence
International Journal of Computer Vision
A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Deformable contours: modeling, extraction, detection and classification
Deformable contours: modeling, extraction, detection and classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Contours: Modeling and Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Multiple Hypothesis Approach to Contour Grouping
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Visual Tracking and Motion Determination Using the IMM Algorithm
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Framework for Model-Based Tracking Experiments in Image Sequences
International Journal of Computer Vision
Tracking method of weak aerial target based on contourlet and mean shift
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Object tracking based on the combination of learning and cascade particle filter
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Robust feature descriptors for efficient vision-based tracking
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A medical tracking system for contrast media
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
A robust template tracking algorithm with weighted active drift correction
Pattern Recognition Letters
Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features
Advanced Engineering Informatics
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This paper presents an object tracking technique based on the Bayesian multiple hypothesis tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation. The segmentation of contours is based on an edge map. The segmented contours are then merged to form recognisable objects. The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. If an acceptable number of key-points lie on or near the contour of the object in a particular frame (n-th frame), we conclude that the selected object has been tracked (identified) successfully in frame n.