مشخصات پژوهش

صفحه نخست /A fast and accurate moving ...
عنوان
A fast and accurate moving object tracker in active camera model
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Moving object, Active camera, Tracking, k-means clustering, DBSCAN clustering, Frame difference
چکیده
Detecting and tracking moving objects within a scene is an essential step for high-level machine vision applications such as video content analysis. In this paper, we propose a fast and accurate method for tracking an object of interest in a dynamic environment (active camera model). First, we manually select the region of the object of interest and extract three statistical features, namely the mean, the variance and the range of intensity values of the feature points lying inside the selected region. Then, using the motion information of the background’s feature points and k-means clustering algorithm, we calculate camera motion transformation matrix. Based on this matrix, the previous frame is transformed to the current frame’s coordinate system to compensate the impact of camera motion. Afterwards, we detect the regions of moving objects within the scene using our introduced frame difference algorithm. Subsequently, utilizing DBSCAN clustering algorithm, we cluster the feature points of the extracted regions in order to find the distinct moving objects. Finally, we use the same statistical features (the mean, the variance and the range of intensity values) as a template to identify and track the moving object of interest among the detected moving objects. Our approach is simple and straightforward yet robust, accurate and time efficient. Experimental results on various videos show an acceptable performance of our tracker method compared to complex competitors.
پژوهشگران ناصر فرج زاده (نفر اول)، عزیز کرمیانی (نفر دوم)، مهدی هاشم زاده (نفر سوم)