ICRA 2011 Paper Abstract

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Paper TuA204.5

Arie, Makoto (chuo university), Moro, Alessandro (University of Trieste), Hoshikawa, Yuma (Chuo University), Ubukata, Toru (Chuo University), Terabayashi, Kenji (Chuo University), Umeda, Kazunori (Chuo University)

Fast and Stable Human Detection Using Multiple Classifiers Based on Subtraction Stereo with HOG Features

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking I" (TuA204), Tuesday, May 10, 2011, 11:05−11:20, Room 3E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Human detection & tracking, Recognition, Computer Vision for Robotics and Automation

Abstract

In this paper, we propose a fast and stable human detection based on “subtraction stereo” which can measure distance information of foreground regions. Scanning an input image by detection windows is controlled in their window sizes and number using the distance information obtained from subtraction stereo. This control can skip a large number of detection windows and leads to reduce the computational time and false detection for fast and stable human detection. Additionally, we propose two-step boosting as a new training way of classifier with whole and upper human body models. Experimental results show that the proposal is faster and less false detection than the recent human detection method with high detection accuracy using HOG (Histogram of Oriented Gradients) features.

 

 

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