An architecture for multiple perspective interactive video
Proceedings of the third ACM international conference on Multimedia
Tracking Human Motion in Structured Environments Using a Distributed-Camera System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
People tracking across two distant self-calibrated cameras
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.10 |
We present a new method of object handoff between two overlapping views in a visual surveillance system. This method requires neither camera calibration nor planar ground assumption. Our approach is composed of two phases: training a handoff table and running object handoff online. In the first phase, a handoff table which contains point correspondences between two views, is constructed from a pair of image sequences. The point correspondences are obtained by computing the conditional probability that for a point pair, given a point belonging to the foreground region at a time instant, the other also belongs to the foreground region at the same time. In the second phase, the actual object handoff is performed with the help of the table, where an optimal bipartite matching algorithm is applied to resolve matching ambiguity. The experimental results for an outdoor scene and synthetic data are shown.