Tracking Objects Across Cameras by Incrementally Learning Inter-camera Colour Calibration and Patterns of Activity

This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method, to model both the colour variations and posterior probability distributions of spatio-temporal links between cameras. These operate in parallel and are then used with an appearance model of the object to track across spatially separated cameras. The approach requires no pre-calibration or batch preprocessing, is completely unsupervised, and becomes more accurate over time as evidence is accumulated.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Andrew Gilbert, Richard Bowden
• Keywords: Batch Preprocessing, Computer Vision, ECCV 2006, Incremental Learning Method, Non-overlapping Cameras, Posterior Probability Distributions