Motion-seeded object-based attention for dynamic visual imagery

David J. Huber; Deepak Khosla; Kyungnam Kim

doi:10.1117/12.2262916

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1 May 2017 Motion-seeded object-based attention for dynamic visual imagery

David J. Huber, Deepak Khosla, Kyungnam Kim

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Proceedings Volume 10202, Automatic Target Recognition XXVII; 102020I (2017) https://doi.org/10.1117/12.2262916
Event: SPIE Defense + Security, 2017, Anaheim, California, United States

Abstract

This paper† describes a novel system that finds and segments “objects of interest” from dynamic imagery (video) that (1) processes each frame using an advanced motion algorithm that pulls out regions that exhibit anomalous motion, and (2) extracts the boundary of each object of interest using a biologically-inspired segmentation algorithm based on feature contours. The system uses a series of modular, parallel algorithms, which allows many complicated operations to be carried out by the system in a very short time, and can be used as a front-end to a larger system that includes object recognition and scene understanding modules. Using this method, we show 90% accuracy with fewer than 0.1 false positives per frame of video, which represents a significant improvement over detection using a baseline attention algorithm.

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David J. Huber, Deepak Khosla, and Kyungnam Kim "Motion-seeded object-based attention for dynamic visual imagery", Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020I (1 May 2017); https://doi.org/10.1117/12.2262916

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