Automatic Pedestrians Segmentation Based on Machine Learning in Surveillance Video*

Abstract

Pedestrian detection and segmentation play an important role in video surveillances. This paper presents a novel pipeline framework for automatic pedestrian detection and segmentation by combining machine learning with traditional computer visual methods. In particular, the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) are employed for pedestrian detection, and then the frame difference method is adopted for the tracking of the pedestrian. GrabCut and Mask R-CNN methods are used in the segmentation of pedestrians. The experiments are conducted on common benchmarks. The experimental results show that our method has made significant progress in automatic pedestrian detection and segmentation compared to the traditional Grabcut method.