An architecture for multiple perspective interactive video
Proceedings of the third ACM international conference on Multimedia
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Understanding human behavior from motion imagery
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Persistent Objects Tracking Across Multiple Non Overlapping Cameras
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An appearance-based approach for consistent labeling of humans and objects in video
Pattern Analysis & Applications
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Object Tracking Using Local PCA
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multi-view-based Cooperative Tracking of Multiple Human Objects in Cluttered Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Fusion of Omnidirectional and PTZ Cameras for Accurate Cooperative Tracking
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Non-overlapping Distributed Tracking System Utilizing Particle Filter
Journal of VLSI Signal Processing Systems
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian-Competitive Consistent Labeling for People Surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved tracking of multiple vehicles using invariant feature-based matching
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Continuous tracking within and across camera streams
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Consistent labeling for multi-camera object tracking
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Socio-economic vision graph generation and handover in distributed smart camera networks
ACM Transactions on Sensor Networks (TOSN)
A method of abnormal habits recognition in intelligent space
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Camera handoff is a crucial step to obtain a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are identified across different cameras. However, there exist many unsolved questions in developing an efficient camera handoff algorithm. In this paper, we first design a trackability measure to quantitatively evaluate the effectiveness of object tracking so that camera handoff can be triggered timely and the camera to which the object of interest is transferred can be selected optimally. Three components are considered: resolution, distance to the edge of the camera's field of view (FOV), and occlusion. In addition, most existing real-time object tracking systems see a decrease in the frame rate as the number of tracked objects increases. To address this issue, our handoff algorithm employs an adaptive resource management mechanism to dynamically allocate cameras' resources to multiple objects with different priorities so that the required minimum frame rate is maintained. Experimental results illustrate that the proposed camera handoff algorithm can achieve a substantially improved overall tracking rate by 20% in comparison with the algorithm presented by Khan and Shah.