Multi-player Detection with Articulated Mixtures-of-Parts Representation Constrained by Global Appearance

Abstract

We describe a new representation for multiple articulated sport players for court player detection. Instead modeling object into deformable parts templates or mixtures of small parts which just capture local appearance of parts and spatial relations between parts, our proposed model trained with local part information with global constraint by a structured SVM can capture not only such local appearance and spatial relations above, but also the semantic relations between body parts which are the critical factors for precisely detecting objects and pose estimation. Our approach has several novel properties: (1) we adopt typical articulated part-based model with global appearance constraint to control trade-off between recall and precision for detection (2) we incorporate semantic knowledge about various articulation of court players into our mixture-of-parts model, these semantic knowledge are popularly used for pose estimation (3) after the root (global) and part (local) bounding boxes are predicted by our system, we train a linear least-squares regression model to output the final detection results which yields considerable improvements in performance. In our experiments with very challenging APIDIS basketball dataset and standard INRIA person dataset, it indicates that our detection system achieves state-of-art performance compared to previous works.