Abstract-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.

Abstract-Human action recognition is an important research area which has captured lot of attention from the research community due to its significant applications. Recently, due to the popularity and successful implementation of deep learning-based methods for image analysis, object recognition, and speech recognition. Researchers are motivated to shift from traditional feature-based approach to deep learning. This research work presents an innovative method for human action recognition using pre-trained Convolutional Neural Networks (CNNs) model as a source architecture for extracting features from the target dataset, followed by a hybrid Support Vector Machines and K-Nearest Neighbor (SVM-KNN) classifier for action classification. It has been observed that already learnt CNN based representations on large-scale annotated dataset are successfully transferable to action recognition task with limited training dataset. The proposed method is evaluated on two well-known action datasets, i.e., UCF sports and KTH. The comparative analysis suggests that the proposed method is better than handcrafted feature-based methods in terms of accuracy.

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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.