Computational geometry: an introduction
Computational geometry: an introduction
Object identification: a Bayesian analysis with application to traffic surveillance
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Self-Organization of Randomly Placed Sensors
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Tracking Many Objects with Many Sensors
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Locating moving entities in indoor environments with teams of mobile robots
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automated multi-camera planar tracking correspondence modeling
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Recovering network topology with binary sensors
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Hi-index | 0.00 |
When a network of vision-based sensors is emplaced in an environment for applications such as surveillance or monitoring the spatial relationships between the sensing units must be inferred or computed for self-calibration purposes. In this paper we describe a technique to solve one aspect of this self-calibration problem: automatically determining the topology and connectivity information of a network of cameras based on a statistical analysis of observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data for systems containing up to 10 agents and verified with experiments conducted on a six camera sensor network.