Fast and accurate object detection using image cropping/resizing in multi-view 4K sports videos - Xu, J.; Kanokphan, L.; Tasaka, K. (MMSports'18 Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports - Seoul, Republic of Korea — October 26 - 26, 2018, 2018)
Recently, fast and accurate DNN object detectors such as YOLO and SSD have attracted considerable attention. However, it still takes far more time than real time processing when inputting a 4K video and becomes even more challenging when inputting multi-view 4K sports videos due to the massive amount of data and small objects. This paper presents a novel approach to significantly accelerate object detection. Observing the fact that object regions (including players and balls) are very sparse in sports videos, we crop the images to greatly reduce the processing areas using the temporal or view correlations. However, simple image cropping may worsen the accuracy if the object is too small to be detected. A further observation is that the detection can be improved by resizing the small objects properly. Our experimental results on two soccer matches of J1 League demonstrate that we can make the detection much faster with a high accuracy for small objects.