ICRA 2011 Paper Abstract


Paper TuA204.4

Wu, Jianxin (Nanyang Technological University), Geyer, Christopher (iRobot Corporation), Rehg, James (Georgia Institute of Technology)

Real-Time Human Detection Using Contour Cues

Scheduled for presentation during the Regular Sessions "Human Detection and Tracking I" (TuA204), Tuesday, May 10, 2011, 10:50−11:05, 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, Computer Vision for Robotics and Automation, Visual Learning


A real-time and accurate human detector, C4, is proposed in this paper. C4 achieves 20 fps speed and state-of-the-art detection accuracy, using only one processing thread without resorting to special hardwares like GPU. Real-time accurate human detection is made possible by two contributions. First, we show that contour is exactly what we should capture and signs of comparisons among neighboring pixels are the key information to capture contours. Second, we show that the CENTRIST visual descriptor is particularly suitable for human detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image pre-processing or feature vector normalization, and only requires O(1) steps to test an image patch. C4 is also friendly to further hardware acceleration. In a robot with embedded 1.2GHz CPU, we also achieved accurate and 20 fps high speed human detection.



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