Tracking and Object Classification for Automated Surveillance

By Omar Javed, Mubarak Shah. European Conference on Computer Vision, 2002

412 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8b588f0b9c8cc866460e979d1970551ad64e6625

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Citations in the corpus

Learning object motion patterns for anomaly detection and improved object detection (2008, methodology): “We have used the object detection and tracking system presented in [8].”
Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views (2008, methodology): “In both phases, the single camera object detection and tracking information is obtained by using the method proposed in [15].”
A hierarchical approach to robust background subtraction using color and gradient information (2002, methodology): “We have already presented a method to remove cast shadows [9] from a scene.”
A survey on activity recognition and behavior understanding in video surveillance (2013, background): “Rao and Shah [147] proposed a viewinvariant trajectory matching method to learn human actions without any training model and to avoid ambiguity of action and their trajectories.”
A survey on visual surveillance of object motion and behaviors (2004, background): “It should be pointed out that this classification is not absolute in that algorithms from different categories can be integrated together [169].”
Computer vision based method for real-time fire and flame detection (2006, unknown): “Image and video content understanding and analysis methods have been studied by many researchers including (Davis and Bobick, 1997; Haering et al., 2000; Javed and Shah, 2002; Naphade et al., 1998).”
Human detection from images and videos: A survey (2016, methodology): “[83], the recurrent motion image (RMI) computed as the sum of changes (on subtracted background images) over a period of time was employed to encode foregrounds undergoing repeated changes.”
Shadow detection: A survey and comparative evaluation of recent methods (2012, background): “normalised cross-correlation [42], gradient or edge correlation [14, 46, 37], orthogonal transforms [48], Markov or conditional random fields [45, 32], Gabor filtering [17]).”