Special Issue Article Open Access

Empirical Evaluation of Detecting Moving Objects Using Graph Cut Segmentation

Abstract

Real-time moving object detection, classification and tracking capabilities is presented with its 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. Our proposed new background model updating method and adaptive thresholding are used to produce a foreground object mask for object tracking initialization. The proposed method to determine the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method update 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 approach achieves 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.

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