Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Face Cataloger: Multi-Scale Imaging for Relating Identity to Location
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Automatic Camera Selection and Fusion for Outdoor Surveillance under Changing Weather Conditions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
VC-Dimension of Exterior Visibility
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target tracking with distributed sensors: the focus of attention problem
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements
Computer Vision and Image Understanding - Special issue on omnidirectional vision and camera networks
Multiple Object Tracking Using Local PCA
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
An adaptive focus-of-attention model for video surveillance and monitoring
Machine Vision and Applications
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera selection in visual sensor networks
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
Online camera selection is introduced as a result of the improved mobility of cameras and the increased scale of surveillance systems. Most existing camera assignment algorithms achieve an optimal observation under the assumption of the unlimited camera computational capacities. However, practical surveillance systems experience resource limitation and see a degradation in the system performance as the number of objects to be processed increases. To address this issue, we propose an adaptive camera assignment algorithm considering the limited camera computational capacities. In so doing, camera resources can be dynamically allocated to multiple objects according to their priorities and the current camera computational load. Experimental results illustrate that the proposed camera assignment algorithm is capable of maintaining a constant frame rate and achieving a substantially decreased object rejection rate in comparison with the algorithm presented by Bakhtari and Benhabib.