# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 1 Introduction  
Although it is simple for humans to perceive and identify activities in videos, automating this process is challenging. Activity recognition is crucial to interpreting videos. Applications of activity recognition include automated surveillance, monitoring elderly behavior, human-computer interaction, content-based video retrieval, and video summarization. Activity recognition can be broken down into the following activities: First, activity classification that aims to categorize an image or video with the appropriate title (such as \"cooking\", \"writing\", etc.). Second, given a specific action and a video as input, action localization attempts to pinpoint the precise location and timestamp in the video at which the action is occurring. Activity recognition has recently been conducted using a variety of techniques, such as wearable sensors, wireless sensors, or network-based sensors. However, compared to other modalities, video-based action recognition systems attract interest due to their high recognition rate and huge availability of video datasets. In this work, we focus on categorizing a video with an appropriate activity title using video-based solutions. A decade ago, researchers mostly relied on features extracted from the motion history image (MH) [1] or optical flow fields [2]. The MHI method represents the motion information of an action in a grayscale image, whereas optical flow represents the displacement of pixels between consecutive frames. These features were fed into classifiers, such as support vector machines (SVMs) or decision trees, to recognize the action. Traditional approaches to HAR relied on handcrafted features and classifiers, such as SVMs and hidden Markov models (HMMs) [3]. These methods were computationally efficient, but their performance was limited by the quality of the handcrafted features. On the other hand, the literature shows that the usage of deep learning enhances activity recognition performance. Due to its enhanced performance in applications such assurveillance, object categorization, biometrics, and a few others, deep learning is receiving a lot of attention in the field of computer vision. Recently, there has been a noticeable rise in the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in the research of HAR [4]. CNNs are commonly used for the extraction of spatial information from video frames, while RNNs are usually used to capture temporal dependencies between features. Graph neural networks (GNNs) and attention methods have also been studied in conjunction with existing methods to achieve better interpretability of human activities in videos [4]. With these methods or techniques, HAR performance has significantly increased, especially for longer videos with complex moves. There are several research papers in the literature that use deep learning for activity recognition, specifically, human action recognition or \"HAR\". For instance, in [5], the authors introduced \"Dynamic Images\", which are temporal representations of videos based on rank pooling, and then use these images on pre-trained CNNs. A framework was developed by the authors of [6] where they combined spatial and temporal contexts in one self-supervised framework without the need for extensive preprocessing. They divided video frames into grids of patches and trained a network to solve jigsaw puzzles on them. Due to the lack sufficient dataset samples at the time, in [7] it was proposed extending existing video datasets by applying action recognition on millions of videos over social platforms and categorizing them into the existing categories on mentioned datasets. In [8], the authors extended on the work conducted by the authors of [7] and proposed a neural operation which encodes spatiotemporal features by imposing a weight-sharing constraint on learnable parameters. They performed 2D convolution along three views of volumetric video data (H \(\times\) W, T \(\times\) H, W \(\times\) T) to learn spatial appearance and temporal motion cues.