Abstract: 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 en-coder-decoder networks are built to extract feature of players.Then,the keypoint heatmap is rendered in fea-ture 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 an-chor-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 re-sults 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 algo-rithms.