# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Datasets and Implementation Details  
In this paper, we evaluate the perfomance of our DA-R3 DCNN method on four publicly available benchmark datasets for human activity recognition tasks: UCF11 [27], HMDB51 [28], UCF50 [29], and UCF101 [30]. These datasets are exclusively created for human activity recognition task, and contain videos collected from different sources and have different lengths, resolutions, and viewpoints of humans actions in the videos. The UCF11 [27] dataset comprises 1640 videos collected from YouTube which are then categorized into 11 distinct action classes of human actions. All videos in the dataset are annotated by action appearance, where each video has a spatial resolution of 320 \(\times\) 240. The HMDB51 [28] dataset is a relatively large dataset, containing 6849 videos, categorized into 51 categories. This dataset has a wide range of variation in camera motion, object scale, view point, and background clutter, which makes it challenging for human action recognition tasks. Videos in this dataset are collected from different sources, including movies, YouTube, Prelinger archive, and Google videos. The UCF50 [29] dataset consists of 6676 realistic videos collected from YouTube, containing human actions performed by different subjects in different environments with varying viewpoints. Videos in this dataset are divided into 50 distinct actions by action appearance in the video. Finally, the UCF101 [30] is the largest dataset amongst the above mentioned datasets, containing 13,320 videos of different human actions. This dataset is the extended version of the UCF50 [29], having comparatively more videos and large variation in actions, categorized into 101 action classes. The number of videos per class in each dataset is approximately 100 to 200, and the duration of video clips is in between 2 and 3 s, with a frame rate of 25 FPS.  
For implementation, we used Python version 3 utilizing Keras with a TensorFlow 2.0 backend. We performed the experiments on a computer system equipped with an Intel(R) Xeon(R) CPU E5-2640, operating at a frequency of 2.50 GHz, and 32 GB of dedicated main memory (RAM). Additionally, we employed two dedicated Tesla GPUs with compute capabilities of 7.5 as hardware resources along with the Nvidia CUDA 11.0 library. To train the proposed DA-R3 DCNN model, we used 70% of data for training, 20% for validation, and 10% for testing the model performance after training. The same data splitting ratio was considered for each dataset used in the experiments of this paper. It is worth mentioning here that each set of data (including training, validation, and test sets) containedall classes, where each class consisted of videos as per their corresponding split ratios (training 70%, validation 20%, and test 10%). Further, for model's weights adjustment and convergence, we employed the Adam optimizer with a fixed learning rate of 0.0001 and utilized categorical cross-entropy loss to adjust the network weights. We set the input sequence length to 16 frames, allowing the DA-R3 DCNN model to extract spatiotemporal information by sliding multiple 3D kernels over the sequence of frames. To obtain and compare the performance of our DA-R3 DCNN method with the state-of-the-art methods, we used two different evaluation metrics: model accuracy performance evaluation and runtime performance evaluation. For accuracy comparison, we compared the average accuracy of our model for each dataset with the state-of-the-art methods, whereas, for runtime performance comparison, we used two metrics: FPS and SPF.