## 2 Activity Recognition-Related Works
### Typically Developmental People
The LSTM and CNN models encode action-deep features for classification. The framework's generality and efficiency in testing runtime are noted, even though it is unsuitable for real-time applications. Furthermore, other authors [15] suggest a novel self-supervised learning approach for video transformers that uses unlabeled video data. In this study, the authors present a method for generating local and global spatiotemporal views from movies of varied spatial sizes and frame rates. The goal is to make the properties of these features invariant to spatiotemporal differences in activities. This technique, which does not require negative samples or specific memory banks, allows the model to interpret films with slow-fast dynamics while still supporting long-term relationship modeling across spatiotemporal dimensions. The technique outperforms activity recognition benchmarks such as Kinetics-400, UCF-101, HMDB-51, and SSv2, and converges quickly even with small batch sizes. This groundbreaking methodology represents a big step toward fast and effective self-supervised learning for video analysis. Moreover, the authors in [16] develop a new framework for HAR that tackles the constraints of previous CNN-based approaches. These constraints include the difficulty of capturing long-range temporal relationships due to limited receptive fields and the high processing demands. The suggested method employs a convolution-free technique that uses a Vision Transformer (ViT) for frame-level spatial feature extraction, followed by multi-layer Long Short-Term Memory (LSTM) networks to capture long-range relationships. Extensive trials on the UCF50 and HMDB51 datasets reveal higher performance, with accuracy gains of 0.944% and 1.414% above the state-of-the-art approaches. The findings demonstrate the framework's ability to handle the problematic characteristics of HAR such as crowded backdrops, shifting views, poor resolution, and partial occlusion in surveillance videos. In addition, ref. [17] introduce a novel framework for HAR that uses transformers. Traditional HAR systems use Convolutional Neural Networks (CNNs) to extract features and recurrent neural networks (RNNs) to forecast temporal sequences. These approaches, however, are computationally demanding and frequently inefficient on resource-constrained systems. The proposed ViT-ReT system uses a Vision Transformer (ViT) to extract frame-level features and a Recurrent Transformer (ReT) to forecast sequences. The ViT-ReT model is intended to increase both speed and accuracy, providing a considerable advantage over conventional techniques. Extensive studies on four benchmark datasets (YouTube Action, HMDB51, UCF50, and UCF101) show that the ViT-ReT framework outperforms the baseline ResNet50-LSTM method in terms of both accuracy and speedup. This makes the ViT-ReT framework an excellent choice for real-time HAR applications in resource-constrained contexts. The authors in [18] present a model that combines CNN spatial feature extraction with ViTs contextual understanding. This hybrid model, known as ConViT, improves the capacity to understand human activities by differentiating the significant bits of a picture from the irrelevant ones. The model is further improved by including a human categorization branch that focuses on certain portions of pictures to help in activity prediction. On the Stanford40 and PASCAL VOC 2012 action datasets, ConViT outperformed classic CNN models and other state-of-the-art approaches, with mean Average Precision (mAP) ratings of 95.5% and 91.5%, respectively. Finally, [PERSON] et al. [19] propose a unique technique for semi-supervised activity recognition utilizing transformer models, solving the issues caused by the high cost of video annotations. To manage unlabeled video samples, the suggested technique, SVFormer, uses a stable pseudo-labeling architecture known as EMA-Teacher. Unlike typical Convolutional Neural Network (CNN) techniques, SVFormer uses transformers to improve performance.