Fine-Grained Soccer Actions Classification Using Deep Neural Network

Action recognition from video data modal is contemplation about recognizing features in spatial position and characterizing the temporal changes across these extracted spatial features. Soccer video action recognition is a categorized field of video action recognition. Soccer sports have enormous viewers across the globe, and there is a significant commercial impact on the broadcasters. High inter-class similarities of various soccer actions symbolize the fine-grained nature of action recognition. Soccer matches are broadcasted live and captured using shifting cameras rather than stationary cameras. The fine-grained nature of soccer actions and shifting camera possessions make the recognition more intricate than the coarse-grained. There exist only limited researches that address this issue. To this extent, we propose a deep learning approach that successfully categorizes ten different soccer actions from our custom-developed SoccerAct10 dataset. Feature extraction is accomplished utilizing transfer learning from state-of-the-art convolutional neural networks (CNN) models such as DenseNet201, InceptionResNetV2, MobileNetV2, ResNet152V2, and Xception. All these models were trained on a massive ImageNet dataset. Long short-term memory (LSTM), with the input features from CNN, models the temporal changes of soccer actions. LSTM has already proven its success in tackling the vanishing gradient. At the final layer, the softmax activation function yields the distributions of probabilities of each soccer action. Empirical evaluation demystifies the effectiveness of our proposed approach, distinguishing ten distinct soccer actions with 90% accuracy.