Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

Service-oriented architecture has evolved to be the backbone for large-scale integration between different applications and platforms. This concept has led to today’s reality of cloud services. Many of the major business platforms are providing their services to end-users and other companies as well. Companies are crafting ways to allow other businesses fast service integration and to get on board quickly in the market. REST (representational state transfer) has emerged as the standard protocol for implementing and consuming these services, which are called RESTful application programming interfaces (APIs). As the internal details of the RESTful APIs are not completely available during consumption, thorough testing has been a major challenge. Any unprecedented change in the APIs can cause the major failure of service operations, which can cause an organization to face both financial and trust losses. Research efforts have been made to alleviate testing challenges by introducing different frameworks and auto-generating unit test approaches. However, there is still a lack of an overview of the state-of-the-art in RESTful API testing. As such, the objective of this article is to identify, analyze, and synthesize the studies that have been performed related to RESTful APIs’ testing methodologies and unit test generation. With this perspective, a systematic literature review (SLR) study was conducted. In total, 16 papers were retrieved and included based on study selection criteria for in-depth analysis. This SLR discusses and categorizes different problems and solutions related to RESTful APIs’ testing and unit test generation.

Different types of research have been done on video data using Artificial Intelligence (AI) deep learning techniques. Most of them are behavior analysis, scene understanding, scene labeling, human activity recognition (HAR), object localization, and event recognition. Among all these, HAR is one of the challenging tasks and thrust areas of video data processing research. HAR is applicable in different areas, such as video surveillance systems, human-computer interaction, human behavior characterization, and robotics. This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively. We propose a new taxonomy for categorizing the literature as CNN and RNN-based approaches. We further divide these approaches into four sub-categories and present various methodologies with their experimental datasets and efficiency. A short comparison is also made with the handcrafted feature-based approach and its fusion with deep learning to show the evolution of HAR methods. Finally, we discuss future research directions and some open challenges on human activity recognition. The objective of this survey is to give the current progress of vision-based deep learning HAR methods with the up-to-date study of literature.