A survey on activity recognition and behavior understanding in video surveillance

By Sarvesh Vishwakarma, Anupam Agrawal. 2013

428 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

RGB-D-based action recognition datasets: A survey (2016, methodology): “Prior to the release of Microsoft Kinect , research has mainly focused on learning and recognizing actions from conventional twodimensional (2D) video [1, 2, 3, 4].”
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, background): “There is a large body of work on human activity recognition in the computer vision literature [1, 21, 29, 36].”
Recognition of Human Actions using Edit Distance on Aclet Strings (2015, background): “The recognition of human actions has attracted in the last years several scientists working in the pattern recognition and computer vision fields (Turaga et al., 2008; Poppe, 2010; Weinland et al., 2011; Vishwakarma and Agrawal, 2013).”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, background): “There is a significant body of research on background subtraction focused on video surveillance [37].”
Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization (2019, background): “Temporal action localization is an important visual task with potential applications in video surveillance [42], video summarization [28], skill assessment [16], and others.”
Exploiting the deep learning paradigm for recognizing human actions (2014, background): “A number of surveys on this topic have been recently proposed [21, 24], which confirms the great interest of the scientific community in this field, although a definitive solution has not been found yet.”
Vision-based human activity recognition: a survey (2020, methodology): “A few number of these works such as [12, 74, 136, 143, 189, 201, 231] provide a comprehensive review on different aspects of HAR methods, while most of them look at HAR task from a specific point of view.”
A Comprehensive Survey of Vision-Based Human Action Recognition Methods (2019, background): “[34] provided a comprehensive survey of human action recognition methods developed from 2008 to 2012.”
Fine-Grained Activity Recognition with Holistic and Pose Based Features (2014, background): “Instead we point to the respective surveys [28,4] and concentrate on the methods most relevant for this work.”
Going deeper into action recognition: A survey (2016, background): “…et al. (2008) surveillance and crowd analysis Poppe (2010) human action recognition Weinland et al. (2011) action recognition Aggarwal (2011) motion analysis fundamentals Metaxas and Zhang (2013) human gestures to group activities Vishwakarma and Agrawal (2013) activity recognition and monitoring”