Real-time human detection using contour cues

Abstract

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 1.2 GHz CPU, we also achieved accurate and 20 fps high speed human detection.