# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 1 Introduction  
Sports activities have an immense impact on a person's mind, body, and spirit. They not only alleviate stress and anxiety, but also fill our soul with positive energy. Accelerated by the tremendous technological growth, sports video data was produced, distributed, and explosively dispersed, becoming an invaluable part of big data of this age. This stream has driven the evolution of upgraded techniques for a wide range of sports video related applications that require a perception of those video contents. Classifying sports videos into different genres or groups is an essential activity, allowing effective indexing and retrieval from extensive video collections. This task can be widely used in recommendation systems that suggest sports videos to the end-users and also in promoting sports commercials in streaming services as per the user's interests. The objective of sports video classification focuses on the automated tagging of sports video clips based on apposite and specific sports action, consisting of spatial and motion features. Computer vision and artificial intelligence technologies are becoming trendy in the study of analyzing videos. However, the domain of computer vision is currently shifting from statistical approaches to deep learning methods due to their efficiency in extracting complex features without human intervention. DNN or deep neural network-based models have been widely used and effectively solve intricate signal processing, machine translation, and computer vision tasks. Therefore, there is a huge scope for commencing innovation and development in this evolving research arena. Furthermore, the two most prominent deep learning networks,i.e., convolutional neural network (CNN) and recurrent neural network (RNN), can capture the spatial and temporal features, respectively, from videos that are obligatory for correctly classifying the video classes. Additionally, sports video classification is quite a novel research interest, and there is still room for improvement and exploration on this ground. The history of Bangladesh encompasses its cultural diversity and literary heritage. Furthermore, significant parts of its profoundly ingrained heritage are reflected in its traditional sports activities. Therefore, sport in Bangladesh is a popular medium of amusement as well as an integral part of Bangladesh culture. However, due to the lack of nurture and retention, these traditional sports activities are losing their density day by day. Classification of traditional Bangladeshi sports videos perhaps can revive the glory and pride of these sports. In this paper, our main concern is to classify five traditional Bangladeshi sports categories, i.e., Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich, from a newly constructed dataset of 500 traditional Bangladeshi sports videos through incorporating the convolutional neural network with the recurrent neural network. A variation of RNN, namely long short term memory (LSTM), is explored and integrated with the CNN architecture. Additionally, we have experimented with a widely used deep learning technique, the transfer learning method, by exploring pretrained VGG16 and VGG19 networks combined with RNN to accomplish the traditional Bangladeshi sports video classification task. Through extensive exploration and evaluation, the optimum model, i.e., the proposed model, has been attained by integrating the fine-tuned VGG19 network with the LSTM layer. In addition, we have extensively assessed our proposed model in the four most challenging and eminent datasets, KTH Sports, UCF-11, UCF Sports, and UCF-101. The proposed model exhibits impressive performance in these datasets relative to some existing approaches. However, the key contributions of this paper are enlisted as follows:  
* Developing a novel Traditional Bangladeshi Sports Video (TBSV) dataset containing 500 traditional Bangladeshi sports videos belonging to five classes: Kabaddi, Boli Khela, Kho Kho, Lathi Khela, and Nouka Baich.