Fully invariant multiple object recognition and tracking using MACH and Kalman filters

Peter Bone; Rupert Young; Chris Chatwin

doi:10.1117/12.711141

9 April 2007 Fully invariant multiple object recognition and tracking using MACH and Kalman filters

Peter Bone, Rupert Young, Chris Chatwin

Proceedings Volume 6574, Optical Pattern Recognition XVIII; 65740A (2007) https://doi.org/10.1117/12.711141
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

A method of recognising and tracking multiple solid objects in video sequences despite any kind of perspective distortion is demonstrated. Moving objects are initially segmented from the scene using a background subtraction method to minimize the search area of the filter. A variation on the Maximum Average Correlation Height (MACH) filter is used to create invariance to orientation while giving high tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of orientations and applied sequentially over the regions of movement in successive video frames to test for target objects. A Kalman filter is employed to continuously track the target objects over successive frames, which has enabled the system to track multiple targets despite temporary occlusion or intersection.

Citation Download Citation

Peter Bone, Rupert Young, and Chris Chatwin "Fully invariant multiple object recognition and tracking using MACH and Kalman filters", Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740A (9 April 2007); https://doi.org/10.1117/12.711141

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