Active Player Detection in Handball Videos Using Optical Flow and STIPs Based Measures

In handball videos recorded during the training, multiple players are present in the scene at the same time. Although they all might move and interact, not all players contribute to the currently relevant exercise nor practice the given handball techniques.The goal of this experiment is to automatically determine players on training footage that perform given handball techniques and are therefore considered active. It is a very challenging task for which a precise object detector is needed that can handle cluttered scenes with poor illumination, with many players present in different sizes and distances from the camera, partially occluded, moving fast. To determine which of the detected players are active, additional information is needed about the level of player activity. Since many handball actions are characterized by considerable changes in speed, position, and variations in the player’s appearance, we propose using spatio-temporal interest points (STIPs) and optical flow (OF). Therefore, we propose an active player detection method combining the YOLO object detector and two activity measures based on STIPs and OF.The performance of the proposed method and activity measures are evaluated on a custom handball video dataset acquired during handball training lessons.

[1]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Luc Van Gool,et al.  An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.

[3]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[4]  M. Pobar,et al.  Detection of the leading player in handball scenes using Mask R-CNN and STIPS , 2019, International Conference on Machine Vision.

[5]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[6]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[7]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[8]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[9]  Miran Pobar,et al.  Building a labeled dataset for recognition of handball actions using mask R-CNN and STIPS , 2018, 2018 7th European Workshop on Visual Information Processing (EUVIP).

[10]  Miran Pobar,et al.  Ball Detection Using Yolo and Mask R-CNN , 2018, 2018 International Conference on Computational Science and Computational Intelligence (CSCI).

[11]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[12]  Miran Pobar,et al.  Object detection in sports videos , 2018, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[13]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[14]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Thomas B. Moeslund,et al.  Selective spatio-temporal interest points , 2012, Comput. Vis. Image Underst..

[18]  Cordelia Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

[19]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[20]  Miran Pobar,et al.  Adapting YOLO Network for Ball and Player Detection , 2019, ICPRAM.

[21]  Stephen M. Smith,et al.  ASSET-2: Real-Time Motion Segmentation and Shape Tracking , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[23]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[24]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.