A Review of Human Activity Recognition Methods

By Michalis Vrigkas, Christophoros Nikou, Ioannis A. Kakadiaris. Frontiers in Robotics and AI, 2015

538 citations recorded by the source corpus.

Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/90a754f597958a2717862fbaa313f67b25083bf9

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Citations in the corpus

Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition (2016, background): “Classifying human actions in real-world videos is an open research problem with many applications in multimedia, surveillance, and robotics [1].”
Kernelized covariance for action recognition (2016, background): “Among these ones, action and activity recognition displays a crucial role in human-robot interaction, autonomous driving vehicles and video-surveillance [2].”