Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation

Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation

Naveenkumar M (National Institute of Technology Trichy, India), Sriharsha K. V. (National Institute of Technology Trichy, India) and Vadivel A (National Institute of Technology Trichy, India)
DOI: 10.4018/978-1-5225-7368-5.ch012

Abstract

This chapter presents a novel approach for moving object detection and tracking based on contour extraction and centroid representation (CECR). Firstly, two consecutive frames are read from the video, and they are converted into grayscale. Next, the absolute difference is calculated between them and the result frame is converted into binary by applying gray threshold technique. The binary frame is segmented using contour extraction algorithm. The centroid representation is used for motion tracking. In the second stage of experiment, initially object is detected by using CECR and motion of each track is estimated by Kalman filter. Experimental results show that the proposed method can robustly detect and track the moving object.
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Object Tracking Using Contour Extraction And Centroid Representation

A primitive experiment is conducted on single object moving with a constant speed and no occlusions. In this perspective a series of videos captured using Nikon COOLPIX 12.0 mega pixels are used for motion analysis. For moving object identification, frame differencing technique is chosen. In this technique, absolute difference between two successive frames i and i+1 is calculated. For each of the differenced image that is obtained in each of the successive iteration, a grey threshold is calculated and is applied. As a result the differenced images are transformed into binary images. To smooth the binary image, normalized box filter is applied and then again grey threshold is calculated. As a result, the perfect binary image is produced. For each of the moving object that is identified in the preprocessed image, a centroid is computed. This centroid represents the moving object in each of the differenced image. Finally a trajectory is drawn by connecting the centroids for all the differenced images. The results obtained after testing the algorithm in a video is presented for discussion. The pseudo code for object detection and tracking using Contour Extraction and Centroid Representation (CECR) is shown below.

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