Player detection method based on scale attention and scale equalization algorithm

Abstract

Introduction: Object detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes.

Methods: This study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce reliance on scale statistics by utilizing a specially created SIoU (Similar to Intersection over Union) label that explicitly represents multi-scale features. This label guides the training of multi-scale attention network modules at two granularity levels. Secondly, an integrated scale equalization algorithm within SIoU labels enhances the detection ability of multi-scale targets in imbalanced samples.

Results and discussion: Comparative experiments conducted on basketball, volleyball, and ice hockey datasets validate the proposed method. The relative optimal approach demonstrated improvements in the detection accuracy of players with smaller and larger scale bounding boxes by 11%, 7%, 15%, 8%, 9%, and 4%, respectively.

Keywords: SIoU; implicit feature fusion; multi-scale target detection; scale attention; scale equalization.