# Traditional Bangladeshi Sports Video Classification Using Deep Learning Method
## 2 Related Work
Carrying out a comparative analysis of several feature extraction methods and classifiers, [PERSON] et al. [12] used CNN and median filter with various window lengths to filter classification results for frame sequences in continuous TV broadcasts. Though their system can be used both in real-time and offline applications, better performance was achieved offline, and misclassification among visually and semantically similar sports was also observed. Furthermore, extensive research was conducted by [PERSON] et al. [13], which prolonged the connectivity of CNN in the time domain with noteworthy performance improvement relative to strong feature-based baselines. They introduced a multiresolution, foveated architecture for speeding up the training, and three types of fusion models were implemented on a dataset of 1 M YouTube videos having 487 sports classes. However, [PERSON] et al. [14] constructed a two-stream convolutional neural network (CNN) pipeline by using both static frames and motion optical flows and implemented them on two baseline datasets, i.e., CCV and UCF-101. [PERSON] et al. [15] established a deep neural network combining RNN and CNN to detect soccer video events and built a dataset named Soccer Semantic Image Dataset (SSID). Finally, RNN is used to map the extracted semantic features to four categories of soccer events, i.e., card, corner, goal, and goal attempt. Likewise, [PERSON] et al. [16] established an action recognition framework on three prominent datasets: UCF-101, YouTube 11 Actions, and HMDB51, through extracting in-depth features from every sixth frame of the videos by utilizing CNN and Bi-directionalLSTM, which yielded improved results over state-of-the-art action recognition methods. On the other hand, the AlexNet architecture was used by [PERSON] et al. [17] for four types of shot classification categorized as close-up, long, medium, and crowd/out-of-the-field shots in field sports videos of cricket and soccer collected from YouTube. Additionally, [PERSON] et al. [18] constructed a hybrid model by combining GMM and Kalman Filters with GRU in classification tasks over KTH, UCF 101, and UCF Sports datasets. This model produced promising results relative to some existing approaches on these datasets. By analyzing the temporal coherence, [PERSON] et al. [19] proposed an attention mechanism based GoogleNet-LSTM model that showed impressive performance to recognize actions in UCF-11, HMDB-51, and UCF-101 datasets. Their method was capable of effectively grabbing the salient areas of actions in videos. However, [PERSON] and [PERSON] [20] used spatio-temporal features learned from CNN based principal component analysis network (PCANet) with bag-of-features and encoding schemes in KTH and UCF Sports action recognition dataset and attained promising results. They calculated temporal templates through short-time motion energy images (ST-MEI) as per frame differencing. Likewise, [PERSON] et al. [21] introduced a novel recurrent attention CNN for focusing the crucial regions in videos to detect actions in UCF-11, HMDB-51, and UCF-101 datasets. Their \"attention-again\" model incorporated the adjacent frames with current one for obtaining the crucial part of the frame. On the other hand, considering the overfitting problem of deep neural networks, [PERSON] et al. [22] proposed an ensemble model named gaNet-C for IoT type-of-traffic forecast problem to detect forecast of active connections and elephant flows of networks. They implemented some variants of gaNet-C, and the proposed two variants hold a built-in regularization feature that reduced the overfitting problem to a great extent. However, a very minimal amount of significant research has been carried out in recognizing traditional Bengali sports. [PERSON] et al.