Analysis of Machine Learning Approaches to Detect Pedestrian Under Different Scale Using Frame Level Difference Feature

Over the last 20 years, automotive technology has advanced to the point where automated systems can currently handle various aspects of vehicle control. Due to traffic congestion, pedestrians are particularly vulnerable and they collide with the vehicle’s front end. In recent years, the legal standards and consumer protection assessments for pedestrian protection have gotten much stronger. Sensor technology that must reliably detect an impact between a vehicle and a person has substantial hurdles as a result. Computer vision-based technologies play an important role in the enhancement strategies of automation industries like the Advanced Driver Assistant System (ADAS) by identifying and tracking people on the road. During the process of pedestrian identification, human characteristics are a key factor in determining accuracy. The extraction of features to identify the pedestrians from the video images is a difficult task. In this paper, a per-frame evaluation methodology is taken in hand to make in depth and insightful comparisons among state of art detection techniques with FLD features. This investigates the detection rates at different scales. The experiments that are conducted with the help of Caltech pedestrian dataset. The recognition accuracy is compared and evaluated by avoiding a higher number of erroneous hits and a higher percentage of miss rates.