Accurate 3D locating and tracking of basketball players from multiple videos
Weiliang Meng, Shibiao Xu, Er Li, Xiangyong Zeng, Xiaopeng Zhang · 2018
With the development of pedestrian detection technologies, existing methods cannot simultaneously satisfy high-quality detection and fast calculation for practical applications, especially for accurate 3D locating and tracking of basketball players. We propose an algorithm which can robustly and automatically locate and track basketball players from multiple videos. After extracting the foregrounds, the voxels in the basketball court space are projected back to the foreground images. Occupied voxels are accumulated and smoothed based on integral space for acceleration. Two Gaussian Mixture Models including Grouping Gaussian Mixture Model(GGMM) and Locating Gaussian Mixture Model(LGMM) are designed for continuous locating and grouping players, and a simple blob detector is employed to handle out-of-bound players. Our algorithm is insensitive to occlusions, shadows, lights and computation errors.