Approach to part using deformable part model in pedestrian detection system

Hye Ji Choi; Nara Shin; Kwang Nam Choi

doi:10.1117/12.2242984

11 July 2016 Approach to part using deformable part model in pedestrian detection system

Hye Ji Choi, Nara Shin, Kwang Nam Choi

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 1001108 (2016) https://doi.org/10.1117/12.2242984
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

Abstract

Histogram of Oriented Gradient (HOG) proposed by Dalal and Triggs is currently the most basic algorithm to detection pedestrian. The algorithm is weak to occlusion, since the algorithm trained by the image of pedestrian full body images as one feature. As a result, the detection rate using HOG feature becomes decreases remarkably. To solve this problem, the paper proposed detection system using Deformable Part-based Model (DPM) just divided two parts of pedestrian data through latent Support Vector Machine (SVM) based machine learning. Experimental results show that proposed approach achieves better performance on detection with high accuracy than existed method [1].

Citation Download Citation

Hye Ji Choi, Nara Shin, and Kwang Nam Choi "Approach to part using deformable part model in pedestrian detection system", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001108 (11 July 2016); https://doi.org/10.1117/12.2242984

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