A New Algorithm for Tracking Objects in Videos of Cluttered Scenes

Andres Alarcon Ramirez,Mohamed Chouikha

doi:10.5121/ijitmc.2013.1207

Andres Alarcon Ramirez, Mohamed Chouikha

Open Access

https://doi.org/10.5121/ijitmc.2013.1207

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Abstract

The work presented in this paper describes a novel algorithm for aut omatic video object tracking based on a process of subtraction of successive frames, where the prediction of the direction of movement of the object being tracked is carried out by analyzing the changing areas generated as result of the object’s motion, specifically in regions of interest defined inside the object being tracked in both the current and the next frame. Simultaneously, it is initiated a minimization process which seeks to determine the location of the object being tracked in the next frame usi ng a function which measures the grade of dissimilarity between the region of interest defined inside the object being tracked in the current frame and a moving region in a next frame. This moving region is displaced in the direction of the object’s motion predicted on the process of subtraction of successive frames. Finally, the location of the moving region of interest in the next frame that minimizes the proposed function of dissimilarity corresponds to the predicted location of the object being trackedin the next frame. On the other hand, it is also designed a testing platform which is used to create virtual scenarios that allow us to assess the performance of the proposed algorithm. These virtual scenarios are exposed to heavily cluttered conditions w here areas which surround the object being tracked present a high variability. The results obtained with the proposed algorithm show that the tracking process was successfully carried out in a set of virtual scenarios under different challenging conditions .

Highlights

Video object tracking can be defined as the detection of an object in the image plane as it moves around the scene
Mean-shift (MS) is another technique of video object tracking that is based on primitive geometric shapes [11]. It is defined a region of interest around the object to be tracked in the current frame, and it is started an iterative process based on comparing the histogram of the region of interest in the current frame with the histograms obtained from candidate regions in the frame where there exist the chances of finding the object being tracked
Once the location of the region of interest has been defined in the first frame of the video sequences, the proposed algorithm updates automatically the location of this region of interest for all the remaining video frames

Summary

Introduction

Video object tracking can be defined as the detection of an object in the image plane as it moves around the scene. This topic has a growing interest for both civilian and military applications, such as automated surveillance, video indexing, human-computer interaction (gesture recognition), meteorology, and traffic management system [1][2][3]. There are two basic problems that a tracking system must resolve: the motion estimation and the matching estimation. The motion estimation predicts the location of the most likely region in the video frame where the object being tracked may be placed.

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Conclusion