One of the important requirements in the object detection and tracking is the extracting of efficient features to trackthe target in video sequence. The feature of colour in image is one of the most visual features widely used. The using ofcolour histogram is the most popular method for representing color feature. One of the problems of using colour histogram to represent feature is its lack of spatial information where it is used to represents statistical distribution of the coloursonly. In this paper a new similarity metric with multi block colour histogram of image is proposed. This metric will be used by an object tracking method where the similarity will be applied to get a decision of choosing the correct solution (location) of the object from many candidate locations

object tracking --- feature extraction --- similarity metric

The aim of an object tracker is to generate the trajectory of an object over timeby locating its position in every frame of the video. In this research, we present anobject contour tracking approach using Generalized Gradient Vector Flow(GGVF). GGVF active contour, or snake, is a dynamic curve that moves within animage domain to capture desired image features. Mostly, GGVF is not sensitive toinitial conditions and converges to the optimal contour. Given an initial contournear the object in the first video frame, GGVF can iteratively converge to anoptimal object boundary. In each video frame thereafter, the resulting contour inthe previous video frame is taken as initialization so the algorithm consists of twosteps. In the first step, the initial contour is applied to the desired object in firstvideo frame. The resulting contour is taken as initialization of the second step,which applies GGVF to current video frame. To evaluate the tracking performance,we applied the algorithm to several real world video sequences. Experimentalresults are provided.

Object tracking --- Generalized gradient vector flow --- Motion-based approach --- Contour-based approach --- Active contours --- Snakes.

Object tracking is an important task within the field of computer vision. Where, it is the process of segmenting an object of interest from a video scene and keeping track of its motion, orientation occlusion to extract useful information. This paper is intended to the improve the measurement accuracy of the closing moving object by using the median filter for denoising and Kalman filter for tracking. At the denoising stage, 70-80% of the noise is reduced by using a median filter. The median filter has been used instead of the Wiener filter because the noise which is assumed is salt and pepper, and it is less complexity than the Wiener filter. While in the tracking stage, the KF has been used as estimated filter. However, the measurements have been improved by 11.27%. The simulation has been done by using Matlab 2014, while the proposal is applied to the real video.

تتبع الاجسام مطلب مهم مجال رؤية الكمبيوتر. حيث، هو عملية تجزئة الجسم من مشهد فيديو وابقاء التتبع لحركته، لاستخراج المعلومات المفيدة. وتهدف هذه الورقة إلى تحسين دقة القياس من للاجسام المتحركة المتقاربة باستخدام المرشح الوسيط للتصفية ومرشح كالمان للتتبع. في مرحلة إزالة الضوضاء، تم تقليل 70-80٪ من الضوضاء باستخدام مرشح وسيط. وقد استخدم المرشح الوسيط بدلا من مرشح وينر لأن الضوضاء المفترضة هي الملح والفلفل، وهو أقل تعقيدا من مرشح وينر. بينما في مرحلة التتبع، تم استخدام مرشح كالمان كمقدر (مخمن). ومع ذلك، تم تحسين القياسات بنسبة 11.27٪. وقد تم إجراء المحاكاة باستخدام ماتلاب 2014، في حين يتم تطبيق الاقتراح على الفيديو الحقيقي.

De-noising filter --- median filter --- Kalman filter --- Object tracking --- مرشح إزالة الضوضاء، مرشح الوسيط، مرشح كالمان، تتبع الاجسام.