Performance analysis for gait in camera networks
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Continuous learning of a multilayered network topology in a video camera network
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Continuously tracking objects across multiple widely separated cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Performance analysis for automated gait extraction and recognition in multi-camera surveillance
Multimedia Tools and Applications
Distributed tracking in a large-scale network of smart cameras
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Traffic modeling and prediction using camera sensor networks
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Traffic modeling and prediction using sensor networks: Who will go where and when?
ACM Transactions on Sensor Networks (TOSN)
Modeling Coverage in Camera Networks: A Survey
International Journal of Computer Vision
Object tracking across non-overlapping cameras using adaptive models
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
Hi-index | 0.01 |
This paper presents a weighted statistical method to learn the environment's topology using a large amount of far field vehicle tracking data collected by multiple, stationary non-overlapping cameras. First, an appearance model is constructed by the combination of normalized color and overall model size to measure the moving object's appearance similarity across the non-overlapping views. Then based on the similarity in appearance, weighted votes are used to learn the temporally correlating information and hence to estimate the mutual information. By exploiting the statistical spatio-temporal information, our method can automatically learn the possible links between disjoint views and recover the topology of the network. The effectiveness of the proposed method is demonstrated by experimental results both on simulated and real video surveillance data.