Memetic algorithms: a short introduction
New ideas in optimization
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recognition of Composite Human Activities through Context-Free Grammar Based Representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A general method for human activity recognition in video
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
On how the computational paradigm can help us to model and interpret the neural function
Natural Computing: an international journal
Conceptual representations between video signals and natural language descriptions
Image and Vision Computing
Pattern Recognition Letters
Multi-dimensional visual tracking using scatter search particle filter
Pattern Recognition Letters
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Radar-based road-traffic monitoring in urban environments
Digital Signal Processing
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.01 |
Visual tracking consists of locating or determining the configuration of a known object at each frame of a video sequence. Usually, the description of the whole scene involves the participation of multiple targets, their movements and interactions, etc., and the scenario particular features. This paper presents a visual tracking system framework oriented to provide a ''near natural language'' description of the involved targets in the scene actions. Our prototype focuses on the detection, tracking and feature extraction of a dynamic number of targets in a scenario along time. The design of any visual tracking system usually needs the injection of human knowledge at each transformed level of description, in order to produce from raw videos a linguistic scene summary. The main aim of this work was to make explicit the knowledge injection needed to link the low-level representations (associated to signals) to the high-level semantics (related to knowledge) in the visual tracking problem. As a result, the emerging semantic necessary at the two transformation level is analysed and presented. We have concentrated on the representation spaces for the memetic algorithm particle filter applied to multiple object tracking in annotated scenarios, oriented to video-based surveillance applications. Finally, some example applications on different surveillance scenarios are presented and discussed.