Algorithm for Detecting Occluded Basketball Players Based on Adaptive Keypoint
Ren Yuan1), Luo Jiangtao2)*, and Liang Xupeng3)
1) (School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065) 2) (Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065) 3) (School of Physical Education, Chongqing University of Posts and Telecommunications, Chongqing 400065)
Player detection is the basis of intelligent analysis of basketball events. Due to the complexity of basketball sports, such as fast player movement and serious occlusion of players, existing object detection techniques cannot afford to achieve accurate detection. To address this issue, an object detection algorithm of basketball player is proposed based on an adaptive keypoint heatmap. First, fully-convolutional encoder-decoder networks are built to extract feature of players. Then, the keypoint heatmap is rendered in feature map through an adaptive variable Gaussian kernel radius. The rendering of the keypoint heatmap adopts an adaptive strategy, in which the Gaussian kernel radius is changed with the width and height of the object, so that the network convergence can be accelerated. With the center point of the player extracted from heatmap, the player’s size, position and other information are retrieved through regression. Since the player is detected based on the center point, eliminating the complicated and time-consuming post-processing procedure in anchor-based detections, it is more conducive to distinguish two different players under occlusion conditions. The effectiveness of the proposed approach is validated on the BasketballPlayer dataset, and the experimental results show that lots of missed detection, misdetection, and low detection accuracy among densely occluded players are significantly improved at the processing rate of 26 frames per second, compared with existing algorithms.