Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

Abstract

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

“…Initially, detection aims to find exciting segment objects in a video. The object finder is usually a classifier that the background searches of picture with the sliding window and marks the secondary‐image by window and each object 7–9 . Discovery of particular objects are affected by the training information set; it should be perfect and surrounded by all probable difference or attitude of the object 10,11 .…”