Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

Digital image acquisition is now a simple task and information in the form of digital images is drastically increasing on social media, which has both positive and negative impacts on a society in many different ways. Advanced user-friendly tools have made it easy to manipulate image content in order to gain illegal advantage or to make false propaganda, and digital images and videos are not acceptable in courts of law as evidence without reliable forensic analysis. A lot of research has been done in order to address this problem and many techniques exist that detect and localize copy-move and splicing forgeries. However, it is very important to know whether these methods are robust, properly modelling the structural changes that have occurred in images due to copy-move and/or splicing forgeries, and can reliably classify a digital image as a genuine or modified image. In this paper, we present an extensive literature review of the state-of-the-art techniques on copy-move and splicing forgeries, highlighting their limitations, and we provide future research directions.