## 2 Related Work
The resulting models have demonstrated superior achievement in video investigation tasks, including action recognition, object tracking, and video retrieval, surpassing the capabilities of 2D CNNs and handcrafted-feature-based methods. This shift towards 3D feature learning exemplifies the adaptability of deep learning architectures to different types of visual data and their effectiveness in capturing intricate patterns within dynamic sequences. To address the challenges of extracting motion cues from repetitive video frames, [PERSON] et al. [51] introduced a two-stream architecture for video action recognition, leveraging CNNs. [PERSON] et al. [20] proposed a method called \"ViT-Ref\", which combines Vision Transformer (ViT) and Recurrent Transformer neural networks for HAR in video data, leveraging the Transformer networks for understanding temporal and spatial features in video sequences. The reported 85.20% accuracy was produced by their model for the JHMDB dataset. [PERSON] et al. [52] proposed a method that considers both spatial and motion features to recognize actions, emphasizing the significance of temporal information through the evaluation of the UCF101 dataset. [PERSON] et al. [28] employed a multi-stream CNN model to identify human-related regions, enabling the recognition of multiple actions in a video and reported an achievement accuracy of 71.17% accuracy with the JHMDB dataset. Other studies, such as those by [25], explored the use of two LSTM-based fused networks to capture spatiotemporal cues in video sequences and achieved 92.2% accuracy with the UCF Sports dataset. Hybrid techniques, as presented in [27], focus on learning multiple features for action recognition through feature fusion [53], and they reported 69.0% accuracy for the UCF11 dataset with their model. While these deep learning approaches excel in capturing short-term temporal information, they may fall short for long-term sequences. The advent of LSTM networks, an advanced version of Recurrent Neural Networks (RNNs), addresses this limitation by encoding long-term dependencies [23]. They achieved 93.2% accuracy with their RNN model. Currently, LSTM networks find widespread use for classifying long sequential data in various domains, including action recognition, speech processing, and weather prediction. Recognizing the challenges posed by redundancy in big data, [PERSON] et al. [54] developed a deep-learning-based method to mitigate redundancy in large datasets and incorporated reinforcement learning for resource allocation based on IoT content-centric principles [55]. This holistic exploration showcases the versatility of deep learning approaches in capturing temporal dependencies and managing big data intricacies across diverse applications. In recent years, some people have used the BiLSTM model to recognize dynamic action and achieved 85.30% accuracy with the UCF11 dataset. Similarly, KFDI [17], Dilated CNN+BiLSTM+RB [18], local-global features+QSVM [19], the dynamic human activity recognition work [20], the 3 DCNN [21], and deep autoencoder+CNN [22] methods yielded accuracy rates of 79.40%, 89.00%, 82.60%, 85.20%, 96.20%, and 92.40%, respectively, for the UCF11 dataset. Despite these advancements, existing systems struggle to achieve satisfactory accuracy for the UCF Sports dataset; [PERSON] et al. employed the GMM+KF+GRNN model and achieved 89.01% accuracy for the UCF Sports dataset [24]. [PERSON] et al. employed CNN+BiLSTM-RB model and achieved 89.00% accuracy for the UCF Sports dataset [18]. Sequentially, many researchers have developed their models and evaluated them with the challenging JHMDB dataset to achieve high accuracy, aiming to deploy a system. [PERSON] et al. employed a deep learning architecture and achieved 67.24% accuracy for the JHMDB dataset [26]. [PERSON] et al. employed two-stream LSTM and achieved 52.70% accuracy after evaluating with the JHMDB dataset [25]. [PERSON] et al.