Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Art gallery theorems and algorithms
Art gallery theorems and algorithms
Building an intelligent camera management system
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Automatic pan-tilt-zoom calibration in the presence of hybrid sensor networks
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Multi-camera calibration, object tracking and query generation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Interactive navigation of multiple agents in crowded environments
Proceedings of the 2008 symposium on Interactive 3D graphics and games
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Motion planning for multitarget surveillance with mobile sensor agents
IEEE Transactions on Robotics
Posing to the camera: automatic viewpoint selection for human actions
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Viewpoint Selection for Human Actions
International Journal of Computer Vision
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Hi-index | 0.00 |
We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.