Abstract
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each match. These events (e.g., passes, shots, fouls) are collected by human operators manually, constituting a considerable cost for data providers in terms of time and economic resources. In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i.e., passes, from video streams. Our model combines a set of artificial neural networks that perform feature extraction from video streams, object detection to identify the positions of the ball and the players, and classification of frame sequences as passes or not passes. We test PassNet on different scenarios, depending on the similarity of conditions to the match used for training. Our results show good classification results and significant improvement in the accuracy of pass detection with respect to baseline classifiers, even when the match’s video conditions of the test and training sets are considerably different. PassNet is the first step towards an automated event annotation system that may break the time and the costs for event annotation, enabling data collections for minor and non-professional divisions, youth leagues and, in general, competitions whose matches are not currently annotated by data providers.
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Notes
- 1.
- 2.
PassNet’s code and data are available at https://github.com/jonpappalord/PassNet.
- 3.
In our experiments, we find that this situation happens for 0.66% of the frames.
- 4.
The application is developed using python framework Flask, and is available at https://github.com/jonpappalord/PassNet.
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This work has been supported by project H2020 SoBigData++ #871042.
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Sorano, D., Carrara, F., Cintia, P., Falchi, F., Pappalardo, L. (2021). Automatic Pass Annotation from Soccer Video Streams Based on Object Detection and LSTM. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_29
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