Abstract
Pedestrian detection has been widely applied in intelligent surveillance and driver assistant systems. The histogram of the oriented gradient (HOG) is the most commonly used feature in pedestrian detection algorithms, which is computationally intensive and results in slow detection speed. This paper proposes a method of pedestrian detection, which is based on color self-similarity (CSS) feature and AdaBoost classifier. The color self-similarity (CSS) feature calculates the ratio of two rectangles to measure the self-similarity in HSV color space, and then the AdaBoost classifier is used to screen out the detection windows containing pedestrian. Tests show that this method has the same detection accuracy and faster detection speed compared with HOG detectors.
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This work is supported by the National Natural Science Foundation of China (Grant: 61376028).
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Shen, Dy., Xu, Mh., Guo, Ay. (2016). A Novel Method of Pedestrian Detection Aided by Color Self-similarity Feature. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 646. Springer, Singapore. https://doi.org/10.1007/978-981-10-2672-0_3
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DOI: https://doi.org/10.1007/978-981-10-2672-0_3
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