An Automated Player Detection and Tracking in Basketball Game

Abstract

Vision-based player recognition is critical in sports applications. Accuracy, efficiency, and Low memory utilization is alluring for ongoing errands, for example, astute communicates and occasion classification. We developed an algorithm that tracks the movements of different players from a video of a basketball game. With their position tracked, we then proceed to map the position of these players onto an image of a basketball court. The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations, so that they can better design mechanisms of defence and attack. Overall, our model has a high degree of identification and tracking of the players in the court. We directed investigations on soccer, basketball, ice hockey and pedestrian datasets. The trial comes about an exhibit that our technique can precisely recognize players under testing conditions. Contrasted and CNNs that are adjusted from general question identification systems, for example, Faster-RCNN, our approach accomplishes cutting edge exactness on three sorts of recreations (basketball, soccer and ice hockey) with 1000×fewer parameters. The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…Recognizing the player's action and classifying the events [29][30][31] in basketball videos helps to analyze the player's performance. Player/ball detection and tracking in basketball videos are carried out in [32][33][34][35][36][37] but fail in assigning specific identification to avoid identity switching among the players when they cross. By estimating the pose of the player, the trajectory of the ball [38,39] is estimated from various distances to the basket.…”

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

“…Previous progress in tracking athlete movements using computer vision-based methods has examined sports where playing field dimensions remain constant across competition arenas. Further, the playing field boundaries in previous research are all rectangular in shape, such as the playing fields and courts encountered in soccer, basketball, and squash [25,[37][38][39][40], serving to greatly simplify the technical processes required to determine the field-relative position of detected athletes [4]. A majority of the existing works has also used stationary cameras that minimises, and in some cases eliminates, issues related to a shifting background, appearance distortions, and camera motion that arise from operator pan, tilt, and zoom functions [10].…”

Player detection method based on scale attention and scale equalization algorithm

“…Stein et al ( 2018 ) suggests the fusion of color histograms with target center points. Additionally, Santhosh and Kaarthick ( 2019 ) introduces the combination of the Deformable Parts Model (DPM) with Scale Invariant Feature Transform (SIFT) keypoints. These methods can significantly enhance the ability to extract explicit features of players through artificially designed operators.…”