A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of statistical data association for motion correspondence
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multistage Fuzzy Control: A Prescriptive Approach
Multistage Fuzzy Control: A Prescriptive Approach
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation
Knowledge-Based Systems
A multitarget tracking video system based on fuzzy and neuro-fuzzy techniques
EURASIP Journal on Applied Signal Processing
Multimodal human-computer interaction: A survey
Computer Vision and Image Understanding
An extended hyperbola model for road tracking for video-based personal navigation
Knowledge-Based Systems
Automatic histogram threshold using fuzzy measures
IEEE Transactions on Image Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optimal edge-based shape detection
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.