Abstract
This paper presents a new kernel-based algorithm for video object tracking called rebound of region of interest (RROI). The novel algorithm uses a rectangle-shaped section as region of interest (ROI) to represent and track specific objects in videos. The proposed algorithm is constituted by two stages. The first stage seeks to determine the direction of the object’s motion by analyzing the changing regions around the object being tracked between two consecutive frames. Once the direction of the object’s motion has been predicted, it is initialized an iterative process that seeks to minimize a function of dissimilarity in order to find the location of the object being tracked in the next frame. The main advantage of the proposed algorithm is that, unlike existing kernel-based methods, it is immune to highly cluttered conditions. The results obtained by the proposed algorithm show that the tracking process was successfully carried out for a set of color videos with different challenging conditions such as occlusion, illumination changes, cluttered conditions, and object scale changes.
Highlights
Video object tracking can be defined as the detection of an object in the image plane as it moves around the scene
The results showed the reliability of the proposed algorithm in a variety of challenging conditions such as occlusion, crowded scenes, illumination changes, and camera movements
It is defined a region of interest, R, in the first frame of the video
Summary
Video object tracking can be defined as the detection of an object in the image plane as it moves around the scene. Cross-correlation [9], on the other hand, was used to implement a face tracking algorithm for video conferencing environment This method compares a region of the image with a known signal extracted from the object of interest, and a measure of similarity is used to determine the exact position of the object being tracked in the frame. Our strategy is based on the analyses of the changes that occur within the object being tracked, itself, ignoring the high variability that commonly is presented in the environment that surrounds the object being tracked This novel strategy makes our algorithm more robust than the existing kernel-based methods in cluttered conditions. This paper is organized as follows: In Section 2, we present the novel proposed algorithm to track an object through video sequences.
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