Adaptive pattern recognition in real-time video-based soccer analysis

TY - JOUR

T1 - Adaptive pattern recognition in real-time video-based soccer analysis

AU - Schlipsing, Marc

AU - Salmen, Jan

AU - Tschentscher, Marc

AU - Igel, Christian

PY - 2017/6/1

Y1 - 2017/6/1

N2 - Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data collection, annotation, and learning as an offline task. A semi-automatic labeling of training data and robust learning given few examples from unbalanced classes are required. We present a real-time system acquiring and analyzing video sequences from soccer matches. It estimates each player's position throughout the whole match in real-time. Performance measures derived from these raw data allow for an objective evaluation of physical and tactical profiles of teams and individuals. The need for precise object recognition, the restricted working environment, and the technical limitations of a mobile setup are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online scenario. Switching between classifiers depending on the amount of training data and available training time improves robustness and efficiency. (2) A proper human-machine interface decreases the number of required operators who are incorporated into the system's learning process. Their main task reduces to the identification of players in uncertain situations. Our experiments showed high performance in the classification task achieving an average error rate of 3 % on three real-world datasets. The system was proved to collect accurate tracking statistics throughout different soccer matches in real-time by incorporating two human operators only. We finally show how the resulting data can be used instantly for consumer applications and discuss further development in the context of behavior analysis.

AB - Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data collection, annotation, and learning as an offline task. A semi-automatic labeling of training data and robust learning given few examples from unbalanced classes are required. We present a real-time system acquiring and analyzing video sequences from soccer matches. It estimates each player's position throughout the whole match in real-time. Performance measures derived from these raw data allow for an objective evaluation of physical and tactical profiles of teams and individuals. The need for precise object recognition, the restricted working environment, and the technical limitations of a mobile setup are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online scenario. Switching between classifiers depending on the amount of training data and available training time improves robustness and efficiency. (2) A proper human-machine interface decreases the number of required operators who are incorporated into the system's learning process. Their main task reduces to the identification of players in uncertain situations. Our experiments showed high performance in the classification task achieving an average error rate of 3 % on three real-world datasets. The system was proved to collect accurate tracking statistics throughout different soccer matches in real-time by incorporating two human operators only. We finally show how the resulting data can be used instantly for consumer applications and discuss further development in the context of behavior analysis.

KW - Human-machine interfaces

KW - Motion analysis

KW - Sports analysis

KW - Supervised learning

U2 - 10.1007/s11554-014-0406-1

DO - 10.1007/s11554-014-0406-1

M3 - Journal article

AN - SCOPUS:84894236570

SN - 1861-8200

VL - 13

SP - 345

EP - 361

JO - Journal of Real-Time Image Processing

JF - Journal of Real-Time Image Processing

IS - 2

ER -