Nowadays, analyzing football videos using computer vision techniques has attracted increasing attention. Significant events detection, football video summarization, football results predictions, statistics etc. are exciting applications in this area. On the other hand, the deep learning approaches are very successful methods for image and video analysis that need much data. Nevertheless, to the best of our knowledge, publicly available datasets in this area are small or individual, which are not enough for such deep learning-based approaches. A public dataset was collected, annotated, and prepared, namely IAUFD^*, to meet this gap for researches in this direction. The IAUFD contains 100,000 real-world images from 33 football videos in 2,508 min, annotated in 10 event categories. These categories include the goal, center of the field, celebration, red card, yellow card, the ball, stadium, the referee, penalty-kick, and free-kick. It is believed that these moments are the basis and useful for any high-level action or event exploration. For a generalization of our dataset, we paid attention to various weather (e.g., sunny, rainy, cloudy etc.), season, time of day, and location. We also used two deep neural networks (VggNet-13 and ResNet-18) to evaluate our proposed dataset as the baseline for future studies and comparison.

 

Link to the paper:     https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ipr2.12543     (It is Open Access)

Link to the Dataset:  http://sites.google.com/view/image-and-video-analysis

 

 

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