Abstract
This paper proposes a fusion stadium positioning algorithm, which uses self-optimizing particle filter to integrate the improved athlete dead reckoning and WiFi position fingerprint algorithm for stadium positioning. In order to determine the initial absolute position of the stadium positioning, for athletes entering the stadium from the outside, a seamless switching algorithm outside the stadium is proposed, using the characteristics of high-altitude satellite GPS to find a suitable switching point as the initial absolute position. If in the stadium, WiFi static positioning determines the initial absolute position. Then, aiming at the problem that the poorly diversified particles cannot be better integrated and localized, a self-optimized particle filter algorithm is proposed. After resampling and retaining high-weight particles, the characteristics of low-weight particles are embedded in the copied high-weight particles. This can improve diversity, and we finally carry out fusion positioning. The target tracking algorithm based on Mean Shift has a fixed-scale tracking window, and the tracking effect of variable-size targets is not ideal. In this paper, an affine transformation algorithm is introduced to improve it. First, we iterate the adjacent image frames in reverse Mean Shift to determine the center position of the target and then use the corner matching method to perform template matching on the target to adjust the size of the tracking window. Through simulation verification, it is proved that the optimized particle filter hybrid tracking algorithm can achieve the ideal result when the target size changes. For the image sequence S1, the tracking window of the 20th frame and the 40th frame has a small offset, but the optimal position can be quickly found by Mean Shift iteration. For the image sequence S2, between the 40th frame and the 60th frame, the target occlusion causes the accuracy of the target template to decrease, and the Bhattacharyya coefficient is at a relatively low value. For the image sequence S3, the tracking effect of the optimized particle filter hybrid tracking algorithm meets the requirements.
Highlights
Because machine vision technology has the advantages of noncontact and long range of action, in recent years, agricultural personnel, factories, hospitals, military, and scientific research institutions have widely used this technology [1]
In order to adapt to the long-term positioning of athletes and improve the positioning accuracy of athletes, this paper proposes a fusion stadium positioning tracking algorithm based on self-optimizing particle filter
With the characteristics of high-altitude satellite GPS, a suitable switching point is found as the initial absolute position
Summary
Because machine vision technology has the advantages of noncontact and long range of action, in recent years, agricultural personnel, factories, hospitals, military, and scientific research institutions have widely used this technology [1]. In order to improve work efficiency, machine vision technology will be used to check the quality of the product and measure the size of the product [2]. In order to adapt to the long-term positioning of athletes and improve the positioning accuracy of athletes, this paper proposes a fusion stadium positioning tracking algorithm based on self-optimizing particle filter. In order to avoid the problem that particles cannot approach the real position of athletes well, this paper proposes a self-optimizing particle filter method. Ird: is article uses self-optimized particle filter integrated seamless switching outside the stadium or Wi-Fi static determination of the initial absolute position of the athletes, the motion equation established by the dead reckoning module of the athletes, and the observation model established by the WiFi position fingerprint module to fully approximate the position of the athletes in the stadium. When the size of the target changes, the fixed tracking window can still accurately represent the template information of the target, achieving a satisfactory tracking effect
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