# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 2 Related Work  
As previously stated, sports video classification is an entirely new arena in the emerging field of deep learning technologies. This concept has drawn the attention of some researchers who were motivated by the novelty of this task. On the other hand, before the evolution of deep learning techniques, various statistical and classical methods were used by some researchers for categorizing sports videos. However, a few existing methodologies are found for classifying Traditional Bangladeshi Sports Video, as the extent of this task is still dormant. In the early stage, various statistical and classical methods were used to extract the features from inputs manually. The extracted features lead to the final classification of different categories of videos. [PERSON] et al. [1] analyzed sensor, audio, and visual data to classify sports categories from user-generated mobile videos and explored multi-user and multimodal data along with a multiclass SVM classifier. However, sports having similar semantic views encountered some misclassifications. Edge features of 500 sports videos obtained from Non-Subsampled Shearlet Transform (NSST) were analyzed by [PERSON] and [PERSON] [2], and KNN was used as a classifier to categorize five sports video types. Additionally, under the ASSAVID system, [PERSON] et al. [3] proposed an automatic sports image classification method through KNN classifier, yet some misclassification was noted. On the other hand, [PERSON] and [PERSON] [4] categorized sports video types from signature heatmaps by Fisherfaces algorithm as PCA, and Euclidean distance as means of classifier focusing only on the sport played in match-like situations. [PERSON] et al. [5] considered audio and visual features captured from the thermal video to classify 180 video sequences of 1 min containing three sports types. Here, four motion features were extracted and integrated with MFCC audio features, and KNN was used as the classifier. However, [PERSON] et al. [6] fused two HMMs representing color and motion features and used the Baum-Welch algorithm to classify 220 minutes of sports video with four genre types. Likewise, [PERSON] et al. [7] proposed a Hidden Markov Model (HMM)-based classification technique integrated with Baum-Welch algorithm to categorize three genres of sports videos where the speed of color changes was computed for each video frame. [PERSON] et al. [8] explored dominant color, global camera motion, and MFCC features for audio-visual analysis of three categories of sports videos. They used the HMM-based model for classifying them. [PERSON] and [PERSON] [9] classified four different events, i.e., goal-kick, placed-kick, shot-on-goal, and throw-in of soccer video dataset through observing ball directions using HMM. Moreover, the time performance of their experiment on the soccer event classification module was also represented. In recent times, Deep Learning methods have been proven to extract complex features from images automatically. In this context, several researchers have worked on the two most recent deep learning techniques, convolutional neural network (CNN) and recurrent neural network (RNN), to extract spatial and temporal features from sports videos. [PERSON] et al. [10] introduced a combined architecture of dilated CNN and RNN for classifying five categories of sports from sequential frames, and the performance of the architecture was analyzed according to different frame sequences. However, more high-level features could not be extracted due to the inability to implement deep architecture with large enough input size because of hardware limitations. Moreover, in the extension of their research work, [PERSON] et al. [11] proposed two types of scratch model, combining CNN and RNN and a transfer learning approach with pre-trained VGG16 model along with RNN on two sets of comprehensive data named SportsC10 and SportsC15, containing 10 and 15 categories of sports, respectively. In this case, the transfer learning approach integrated with RNN came up with the most promising performance.