## 3 Proposed Method
#### 4.1.1 YouTube Actions or UCF11 Dataset

The dataset of YouTube Actions is one the most-challenging and complex due to the collection of action samples at low resolution, utilizing both moving and stationary cameras at varying scales. Furthermore, the presence of a cluttered background, alterations in illumination, and changes in viewpoint further contribute to the complexity of the dataset. This dataset contains 11 distinct categories of physical movements obtained from various sporting activities such as \"Basketball\", \"Biking\", \"Diving\", \"GolfSwing\", \"HorseRiding\", \"SoccerJuggling\", \"Swing\", \"TennisSwing\", \"TrampolineJumping\", \"VolleyballSpiking\", and \"WalkingDog\". These actions were captured through video recordings involving 25 individuals, with four samples for each action, while additional videos were collected from YouTube [63; 66]. #### 4.1.2 Joint-Annotated Human Motion Data Base (JHMDB) Dataset

The JHMDB dataset comprises 21 distinct categories of activities: catching, clamping, clapping, hair brushing, baseball swinging, gunshot firing, jumping, and more. This dataset is characterized by a substantial collection of 923 videos featuring diverse actions, making it a challenging recognition task. Despite the various challenges and difficulties, the JHMDB

Figure 2:3 Visual depictions sample action classes from the UCF11, UCF Sports, and J-HMDB datasets. dataset achieved low performance compared to other datasets; however, the recognition rate is effective compared to SOTA approaches [65; 67]. #### 4.1.3 UCF Sports Dataset

The UCF Sports dataset contains 150 sequences of videos with a resolution of \(720\times 480\), which are gathered from diverse sporting activities such as diving, horse riding, golf swings, skateboarding, and weightlifting. These sports-related action videos are composed by various outlets, including the BBC and ESPN, and are commonly transmitted via television channels. The videos represent authentic and unscripted actions from different perspectives and a wide array of scenes [68; 69; 70]. ### Experimental Setup

The hardware architectures are very important to facilitate efficient model training. Deep learning models contain high-level interface libraries, such as TensorFlow [71], etc., which are responsible for executing computationally demanding operations. In our particular scenario, NVIDIA was selected as the preferred choice for deep learning models due to its exceptional software support for computations specific to deep learning. All evaluation outcomes were obtained using an NVIDIA GeForce RTX 3090 graphics card. The suggested neural network architecture was encoded within a Python environment, employing the Keras and TensorFlow frameworks. The process involved the utilization of the deep learning toolbox ConvNet\({}^{\prime}\) for extracting CNN features, including MobilNetV2, the neural network libraries for Deep BiLSTM. The proposed approach under consideration was evaluated through the use of two distinct metrics, the accuracy and confusion matrix, as provided for each dataset. The outcomes for each dataset, along with the corresponding comparisons made against the state-of-the-art (STOA), are provided for each dataset. ### Performance Accuracy and SOTA Comparison with UCF11 or YouTube Actions Dataset

The quantitative comparison of the proposed model and SOTA methods for the UCF11 or YouTube Actions dataset is shown in Figure 2:4

The comparative methods included hierarchical clustering [14], single-stream CNN [72], BT-LSTM [16], KFDI [17], dilated CNN+BiLSTM+RB [18], local-global features + QSVM [19], the 3 DCNN [21], the deep autoencoder with CNN [22] and ViT-ReT-based models [20], which attained accuracy rates of 89.7%, 93.1%, 85.3%, 79.4%, 89.0%, 82.6%, 96.2%, 92.4%, and 89.20%, respectively. Our proposed method outperformed these approaches by nearly 3%, leading to improved results.