A buyer's guide to conic fitting
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Intelligent Spaces — The Vision, the Opportunities and the Barriers
BT Technology Journal
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Automatic detection of salient objects and spatial relations in videos for a video database system
Image and Vision Computing
Shape from inconsistent silhouette
Computer Vision and Image Understanding
Pattern Recognition Letters
Tracking multiple people in the context of video surveillance
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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This paper aims to address two of the key research issues in computer vision — the detection and tracking of multiple objects in the cluttered dynamic scene — that underpin the intelligence aspects of advanced visual surveillance systems aiming at automated visual events detection and behaviour analysis. We discuss two major contributions in resolving these problems within a systematic framework. Firstly, for accurate object detection, an efficient and effective scheme is proposed to remove cast shadows/highlights with error corrections based on a conditional morphological reconstruction. Secondly, for effective tracking, a temporal-template-based tracking scheme is introduced, using multiple descriptive cues (velocity, shape, colour, etc) of the 2-D object appearance together with their respective variances over time. A scaled Euclidean distance is used as the matching metric, and the template is updated using Kalman filters when a matching is found or by linear mean prediction in the case of occlusion. Extensive experiments are carried out on video sequences from various real-world scenarios. The results show very promising tracking performance.