Effective Pedestrian Detection using Deformable Part Model based on Human Model

Effective Pedestrian Detection using Deformable Part Model based on Human Model

Recently, pedestrian detection systems have become an important technology in the development of theadvanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) iscurrently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as onesingle feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOGdecreases 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-learningtechnique. Experimental results show that our approach achieves better performance in a detection system than theexisting 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 occlusionscan increase the false detection rate and cause difficulties during the detection process. Our proposed method usesa different approach and can easily be applied in real-world scenarios, regardless of occlusions.

Recently, pedestrian detection systems have become an important technology in the development of theadvanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) iscurrently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as onesingle feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOGdecreases 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-learningtechnique. Experimental results show that our approach achieves better performance in a detection system than theexisting 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 occlusionscan increase the false detection rate and cause difficulties during the detection process. Our proposed method usesa different approach and can easily be applied in real-world scenarios, regardless of occlusions.