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Efficient Feedforward Categorization of Objects and Human Postures with Address-Event Image Sensors

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6 Author(s)
Shoushun Chen ; Sch. of Electr. & Electron. Eng. (EEE), Nanyang Technol. Univ., Singapore, Singapore ; Akselrod, P. ; Bo Zhao ; Perez-Carrasco, J.A.
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This paper proposes an algorithm for feedforward categorization of objects and, in particular, human postures in real-time video sequences from address-event temporal-difference image sensors. The system employs an innovative combination of event-based hardware and bio-inspired software architecture. An event-based temporal difference image sensor is used to provide input video sequences, while a software module extracts size and position invariant line features inspired by models of the primate visual cortex. The detected line features are organized into vectorial segments. After feature extraction, a modified line segment Hausdorff-distance classifier combined with on-the-fly cluster-based size and position invariant categorization. The system can achieve about 90 percent average success rate in the categorization of human postures, while using only a small number of training samples. Compared to state-of-the-art bio-inspired categorization methods, the proposed algorithm requires less hardware resource, reduces the computation complexity by at least five times, and is an ideal candidate for hardware implementation with event-based circuits.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 2 )

Date of Publication:

Feb. 2012

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