Abstract
This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.
Highlights
In recent years, automatic human activity recognition has drawn much attention in the field of video analysis technology due to the growing demands from many applications, such as surveillance environments, entertainment environments and healthcare systems
The local appearance context (LAC) descriptors are first computed on the locations of human objects in an image based on histogram of oriented gradient (HOG), and principal component analysis (PCA) is further applied for dimensionality reduction, followed by the histogramming of LAC to obtain histogram of local appearance context (HLAC)
Progress in recent video-based human activity recognition has been encouraging, there are still some apparent performance issues that make it challenging for real-world deployment
Summary
Automatic human activity recognition has drawn much attention in the field of video analysis technology due to the growing demands from many applications, such as surveillance environments, entertainment environments and healthcare systems. In the first level of core technology, three main processing stages are considered, i.e., object segmentation, feature extraction and representation, and activity detection and classification algorithms. In the third stage of the core technology, the activity detection and classification algorithms are used to recognize various human activities based on the represented features. Feature representation, classification stages in the low-level core technology, the three main aspects of the mid-level human activity recognition systems are discussed, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition.
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