Abstract
Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video's timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.
Highlights
In this paper, we present the method to modify the state model according to the direction of predicting that an object appears in the same direction of motion with a higher probability
We present the experimental particle filter model and present a suggestion for integrating information on the direction of the object’s movement, the Nparticle filter model, to track each part combines
Related Work e correlation filters approach is a powerful tool in digital signal processing [8, 9]. is algorithm class utilizes the properties of Fourier transform of turning convolution in the spatial domain into function multiplication in the Fourier domain [10,11,12,13]. e original idea of the correlation filter was to solve the problem of locating an object in an image
Summary
If a part of the object has the same color and brightness level as any other object in the frame, the tracking may be distorted To fix this problem, we propose to divide the object into many parts, each of which will have the same properties. Because each object moves in a specific trajectory, the direction of the object’s motion will remain constant for a certain period of time.
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