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Proceedings Paper

Real-time violent action detector for elevator
Author(s): Kentaro Hayashi; Makito Seki; Takahide Hirai; Takeuchi Koichi; Sasakawa Koichi
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Paper Abstract

This paper presents a new critical event detection method simplified for an embedded appliance mounted on an elevator car. We first define that the critical event is unusual action such as violent action, counteraction, etc, and introduce the violent action degree(VAD). We use an optical flow based method to analyze the current state of the motion through an ITV(Industrial TeleVision) camera. After motion analysis, we calculate a normalized statistical value, which is the VAD. The statistical value is the multiple of the optical flow direction variance, the optical flow magnitude variance, and optical flow area. Our method calculates the statistical value variance and normalize it by the variance. At last we can detect critical event by thresholding the VAD. Then we implement this method on an embedded appliance. The appliance has an A/D converter with special designed frame buffer, a 400MIPS high performance micro processor, dynamic memory, and some flash ROM. Since we need to process the method 4Hz or faster to keep the detection performance, we shrink the images into 80 by 60 size, adopt the recursive correlation method, and analyze optical flows. The special designed frame buffer enables us for capturing sequencial two images at any time. After that we achieve about 8Hz processing performance on it. Our method detects 80% of critical events where at most 6% of false acception.

Paper Details

Date Published: 6 December 2005
PDF: 8 pages
Proc. SPIE 6051, Optomechatronic Machine Vision, 60510R (6 December 2005); doi: 10.1117/12.648790
Show Author Affiliations
Kentaro Hayashi, Mitsubishi Electric Corp. (Japan)
Makito Seki, Mitsubishi Electric Corp. (Japan)
Takahide Hirai, Mitsubishi Electric Corp. (Japan)
Takeuchi Koichi, Mitsubishi Electric Corp. (Japan)
Sasakawa Koichi, Mitsubishi Electric Corp. (Japan)


Published in SPIE Proceedings Vol. 6051:
Optomechatronic Machine Vision
Kazuhiko Sumi, Editor(s)

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