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pro vyhledávání: '"Kumaran, Santhosh Kelathodi"'
Publikováno v:
ACM Computing Surveys (2020), 6(53):Article 119, 2020
Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. In this paper, we present a survey on relevant visual surveillance related researches for anomaly detection in public places, fo
Externí odkaz:
http://arxiv.org/abs/1901.08292
Classifying time series data using neural networks is a challenging problem when the length of the data varies. Video object trajectories, which are key to many of the visual surveillance applications, are often found to be of varying length. If such
Externí odkaz:
http://arxiv.org/abs/1812.07203
Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Anoth
Externí odkaz:
http://arxiv.org/abs/1804.06680
Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unusual routes taken by commuters/ve
Externí odkaz:
http://arxiv.org/abs/1803.06613
Publikováno v:
Expert Systems with Applications Volume 118, 15 March 2019, Pages 169-181
Accurate prediction of traffic signal duration for roadway junction is a challenging problem due to the dynamic nature of traffic flows. Though supervised learning can be used, parameters may vary across roadway junctions. In this paper, we present a
Externí odkaz:
http://arxiv.org/abs/1803.06480
Autor:
Kumaran, Santhosh Kelathodi, Mohapatra, Shrohan, Dogra, Debi Prosad, Roy, Partha Pratim, Kim, Byung-Gyu
Publikováno v:
In Expert Systems With Applications 15 November 2019 134:267-278
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems; Aug2022, Vol. 23 Issue 8, p11891-11902, 12p
Publikováno v:
In Pattern Recognition Letters 1 December 2019 128:211-219
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