|Effective Pedestrian Detection using Deformable Part Model based on Human Model
Hye Ji Choi, Yoon Suk Lee, Duk-Sun Shim, Chan Gun Lee, and Kwang Nam Choi*
International Journal of Control, Automation, and Systems, vol. 14, no. 6, pp.1618-1625, 2016
Abstract : "Recently, pedestrian detection systems have become an important technology in the development of the
advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is
currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one
single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG
decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM)
that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning
technique. Experimental results show that our approach achieves better performance in a detection system than the
existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example,
the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions
can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses
a different approach and can easily be applied in real-world scenarios, regardless of occlusions."
Keyword : Advanced driver assistance system, deformable part model, histogram of oriented gradients, object detection, pedestrian detection.