Enhanced Basketball Target Tracking in Panoramic Vision Systems via Advanced YOLO and Camshift Algorithm Integration
Abstract
The panoramic vision basketball robot can not only provide a 360° panoramic view, but also obtain real-time position and distance information of the basketball. To improve the target tracking performance of panoramic vision basketball robots, a target detection algorithm is first designed as the basis for subsequent robot target tracking. In object detection, the You Only Look Once version 8 is adopted. A bidirectional feature pyramid network and an improved cross stage partial 2-fusion are introduced for optimization. Based on the improved MeanShift algorithm, namely the continuous adaptive mean shift algorithm, target tracking is achieved. Subsequently, the research introduces Kalman filtering algorithm, target template update judgment, and fused features to optimize the target tracking algorithm. In terms of evaluation indicators, the study selects precision, recall, time consumption, central error, missed detection rate, and false detection rate, and conducted practical application tests. The precision, recall, and time consumption were 98.67%, 97.65%, and 54.21ms, respectively. Under obstructions, lighting changes, and background interference, the precision of the tracking algorithm was 95.67%, 96.47%, and 95.26%, respectively. The maximum central errors were 24 pixels, 21 pixels, and 22 pixels, respectively. The designed object detection and tracking algorithm has good performance and can provide solid technical support for the application of panoramic vision basketball robots in actual competitions.References
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