International Journal of Computer Vision
An architecture for multiple perspective interactive video
Proceedings of the third ACM international conference on Multimedia
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Human Motion Using Multiple Cameras
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
P-Channels: Robust Multivariate M-Estimation of Large Datasets
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A unifying framework for mutual information methods for use in non-linear optimisation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A stochastic approach to tracking objects across multiple cameras
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Multi-view Video Analysis of Humans and Vehicles in an Unconstrained Environment
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Distributed tracking in a large-scale network of smart cameras
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Building semantic scene models from unconstrained video
Computer Vision and Image Understanding
Quorum based image retrieval in large scale visual sensor networks
ADHOC-NOW'12 Proceedings of the 11th international conference on Ad-hoc, Mobile, and Wireless Networks
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This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated cameras with non overlapping fields of view. The approach relies on the three cues of colour, relative size and movement between cameras to describe the relationship of objects between cameras. This relationship weights the observation likelihood for correlating or tracking objects between cameras. Any individual cue alone has poor performance, but when fused together, a large boost in accuracy is gained. Unlike previous work, this paper uses an incremental technique to learning. The three cues are learnt in parallel and then fused together to track objects across the spatially separated cameras. The colour appearance cue is incrementally calibrated through transformation matrices, while probabilistic links, modelling an object's bounding box, between cameras represent the objects relative size. Probabilistic region links between entry and exit areas on cameras provide the cue of movement. The approach needs no pre colour or environment calibration and does not use batch processing. It works completely unsupervised, and is able to become more accurate over time as new evidence is accumulated.