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Naresh P. Cuntoor
Researcher at Kitware
Publications - 26
Citations - 2281
Naresh P. Cuntoor is an academic researcher from Kitware. The author has contributed to research in topics: Hidden Markov model & Gait (human). The author has an hindex of 15, co-authored 25 publications receiving 2089 citations. Previous affiliations of Naresh P. Cuntoor include University of Maryland, College Park & Research Triangle Park.
Papers
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Proceedings ArticleDOI
A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xioyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A new large-scale video dataset designed to assess the performance of diverseVisual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage is introduced.
Journal ArticleDOI
Identification of humans using gait
Amit Kale,Aravind Sundaresan,A. N. Rajagopalan,Naresh P. Cuntoor,Amit K. Roy-Chowdhury,Volker Krüger,Rama Chellappa +6 more
TL;DR: A view-based approach to recognize humans from their gait by employing a hidden Markov model (HMM) and the statistical nature of the HMM lends overall robustness to representation and recognition.
Book ChapterDOI
Gait analysis for human identification
TL;DR: The dynamic time-warping (DTW) approach is used for matching so that non-linear time normalization may be used to deal with the naturally-occuring changes in walking speed.
Proceedings ArticleDOI
Gait-based recognition of humans using continuous HMMs
TL;DR: This paper proposes a view-based approach to recognize humans through gait using a continuous hidden Markov model that serves to compactly capture structural and transitional features that are unique to an individual.
Proceedings ArticleDOI
AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xiaoyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A concept for automatic construction site monitoring by taking into account 4D information (3D over time), that is acquired from highly-overlapping digital aerial images, which largely supports automated methods toward full scene understanding.