A Multi-stage Competitive Neural Networks Approach for Motion Trajectory Pattern Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Robust object segmentation using probability-based background extraction algorithm
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
Syntactic matching of trajectories for ambient intelligence applications
IEEE Transactions on Multimedia
Integrated video object tracking with applications in trajectory-based event detection
Journal of Visual Communication and Image Representation
A micro wireless video transmission system
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Detecting moving targets from traffic video based on the dynamic background model
Transactions on edutainment VI
Moving cast shadow detection and removal for visual traffic surveillance
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Decision fusion for target detection using multi-spectral image sequences from moving cameras
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A probabilistic integrated object recognition and tracking framework
Expert Systems with Applications: An International Journal
Advanced formation and delivery of traffic information in intelligent transportation systems
Expert Systems with Applications: An International Journal
Mobile video surveillance systems: an architectural overview
Mobile Multimedia Processing
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Synthetic ground truth dataset to detect shadows cast by static objects in outdoors
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Planar shape representation and matching under projective transformation
Computer Vision and Image Understanding
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A system for real-time object recognition and tracking for remote video surveillance is presented. In order to meet real-time requirements, a unique feature, i.e., the statistical morphological skeleton, which achieves low computational complexity, accuracy of localization, and noise robustness has been considered for both object recognition and tracking. Recognition is obtained by comparing an analytical approximation of the skeleton function extracted from the analyzed image with that obtained from model objects stored into a database. Tracking is performed by applying an extended Kalman filter to a set of observable quantities derived from the detected skeleton and other geometric characteristics of the moving object. Several experiments are shown to illustrate the validity of the proposed method and to demonstrate its usefulness in video-based applications