A
Aravind Sundaresan
Researcher at University of Maryland, College Park
Publications - 27
Citations - 1719
Aravind Sundaresan is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Motion capture & Motion estimation. The author has an hindex of 16, co-authored 26 publications receiving 1664 citations. Previous affiliations of Aravind Sundaresan include Artificial Intelligence Center & SRI International.
Papers
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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.
Proceedings ArticleDOI
A hidden Markov model based framework for recognition of humans from gait sequences
TL;DR: In this paper, the binarized background-subtracted image is used as the feature vector and different distance metrics, such as those based on the L/sub 1/ and L/ sub 2/ norms of the vector difference, and the normalized inner product of the vectors, are used to measure the similarity between feature vectors.
Journal IssueDOI
Mapping, navigation, and learning for off-road traversal
Kurt Konolige,Motilal Agrawal,Morten Rufus Blas,Robert C. Bolles,Brian P. Gerkey,Joan Sola,Aravind Sundaresan +6 more
TL;DR: The main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control are described.
Journal IssueDOI
Leaving Flatland: Efficient real-time three-dimensional perception and motion planning
Radu Bogdan Rusu,Aravind Sundaresan,Benoit Morisset,Kris Hauser,Motilal Agrawal,Jean-Claude Latombe,Michael Beetz +6 more
TL;DR: The proposed system includes comprehensive localization, mapping, path planning, and visualization techniques for a mobile robot to operate autonomously in complex three-dimensional indoor and outdoor environments and is shown to be favorable for high-speed autonomous navigation.
Patent
Method and system for markerless motion capture using multiple cameras
TL;DR: In this paper, a bottom-up approach is proposed in order to build a parametric (spline-based) representation of a general articulated body in the high dimensional space followed by a top-down probabilistic approach that registers the segments to an average human body model.