With the upgrade of the industry, robots urgently need to track moving targets at high speed. Therefore, the detection algorithms in machine vision technology need to be improved. Aiming at the problem that high and low thresholds need to be fixed in traditional Canny edge detection algorithm, an improved dynamic double threshold Canny algorithm is proposed. Constantly increasing the size of the threshold, Using the size of the area where the image edge is closed as a standard, finally to determine the best threshold, In order to achieve the best detection effect. Experimental results show that, Improved dynamic double threshold Canny algorithm not only improves the edge detection effect by 9% on average compared with the traditional algorithm, but also detects more complete image information and has stronger adaptability.
Machine vision is now widely used. The traditional Meanshift algorithm can easily cause the target to be lost due to its fixed core radius, which makes the target change in size and direction, thus affecting the whole tracking result. Aiming at these problems, this paper introduces the fuzzy control method, based on the adaptive fuzzy mechanism of the similarity function value to select the appropriate dynamic kernel radius in real time, thus improving the tracking effect. Through the comparison of the tracking accuracy before and after the improvement of the video stream of the basketball game, the average accuracy of the improved tracking can reach 73.76%, which is 9.28%, so the target tracking of the athletes on the sports field can be effectively realized.
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