Abstract
Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.
Highlights
Object recognition and tracking is still a major area of interest when it comes to digital image processing, pattern recognition, convolution neural networks and artificial intelligence [1]
The difference of Gaussian (DoG) filter reduces the noise by applying the Gaussian motion blur and enhances the edges, which can be considered a major advantage when it comes to an image processing application [37]
Since the gradient descent technique is known as the optimization algorithm, the results of the proposed algorithm will be compared with other state-of-theart algorithms
Summary
Object recognition and tracking is still a major area of interest when it comes to digital image processing, pattern recognition, convolution neural networks and artificial intelligence [1]. Object tracking has many unique challenges associated with it, such as dealing with variations in scaling [9], occlusion [10], shift [11], camera axis orientations [12], etc Training tracking algorithms, such as approximate proximal gradient methods [13]. The tuning and assignment of weights are of utmost importance since they are the ones that mostly result in accurate prediction and estimation processes. For this purpose, a deep neural network technique known as gradient descent has been employed, which focuses on setting the weight of the parameters based on the lowest loss function
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