A Comparative Review of Recent Kinect-Based Action Recognition Algorithms

Abstract

Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depthbased versus skeleton-based features. In this paper, we analyze and compare ten recent Kinect-based algorithms for both crosssubject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that skeleton-based features are more robust for cross-view recognition than depth-based features, and that deep learning features are suitable for large datasets.

A combined multiple action recognition and summarization for surveillance video sequences

“…There are other alternatives to handle this problem like the use of a Kinect camera [57], that can be helpful for recognition of the action as well as the summarization of it. But, the occlusion and action recognition in a crowd scene still represent a challenge [58,[62][63][64].…”

Multi-Modal Human Action Recognition With Sub-Action Exploiting and Class-Privacy Preserved Collaborative Representation Learning

“…''Human action or activity recognition has played significant roles in many potential applications, including security surveillance, human-computer interaction (HCI), health monitoring and intelligent transportation [1]- [6]. For instance, in healthcare environments, by monitoring the behavior of people and recognizing human activities, the activity habits and patterns of people can be understood.…”

Global Spatio-Temporal Attention for Action Recognition Based on 3D Human Skeleton Data

“…Human action recognition has a wide range of applications [1], such as human-computer interaction, video surveillance, health care, entertainment, etc. Its application has become one of the research hotspots in the field of computer vision [2].…”