Effective pedestrian detection using deformable part model based on human model

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.

     

Object motion tracking using a moving direction estimate and color updates

This paper presents a direction detection and tracking object color update algorithm used to track moving objects that change colors. Different from traditional color-based tracking methods, which use an initial color distribution in order to track objects as long as the object carries the full or partial initial color, this method introduces a color update method used to quickly find the new object color in a new location if the object changes its color partially or completely; the updated color is then used to locate the object. In our algorithm, an initial color pattern is used to track an object using the color. During the tracking, an object’s new location is at first estimated and then used to detect any color change. If the color has changed, a new color pattern is updated based on the changes in the previous color distribution, and then the new color pattern is used to calculate the current location of the object. This algorithm utilizes the property that the movement of an object can be estimated either by using the object’s shadow or by background subtraction. The implementation of our algorithm results in an effective real-time object tracking. The validity of the approach is illustrated by the presentation of experiment results obtained using the methods described in this paper.     

Visual Distinctiveness Detection of Pedestrian based on Statistically Weighting PLSA for Intelligent Systems

International Journal of Control, Automation and Systems - Intelligent Systems for autonomous vehicles including drone, robot vision, and video surveillance, need to distinguish pedestrian from...