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Anurag Mittal
Researcher at Indian Institute of Technology Madras
Publications - 97
Citations - 4413
Anurag Mittal is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Object detection & Pose. The author has an hindex of 31, co-authored 97 publications receiving 3961 citations. Previous affiliations of Anurag Mittal include Cornell University & Princeton University.
Papers
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Proceedings ArticleDOI
Motion-based background subtraction using adaptive kernel density estimation
Anurag Mittal,Nikos Paragios +1 more
TL;DR: A new method for the modeling and subtraction of scenes that consist of static or quasi-static structures that exhibits a persistent dynamic behavior in time is proposed and extensive experiments demonstrate the utility and performance of the proposed approach.
Journal ArticleDOI
M 2 Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
Anurag Mittal,Larry S. Davis +1 more
TL;DR: A system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized surveillance cameras located far from each other and the use of occlusion analysis to combine evidence from different camera pairs is presented.
Proceedings ArticleDOI
A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders
TL;DR: In this paper, a conditional variational autoencoder (CVAE) is used to generate the samples from the given attributes and use the generated samples for classification of the unseen classes.
Proceedings ArticleDOI
Automated feature extraction for early detection of diabetic retinopathy in fundus images
TL;DR: A new constraint for optic disk detection is proposed where the major blood vessels are first detected and the intersection of these are used to find the approximate location of the optic disk.
Book ChapterDOI
M2Tracker: A Multi-view Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo
Anurag Mittal,Larry S. Davis +1 more
TL;DR: A system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far from each other and a scheme for combining evidences gathered from different camera pairs using occlusion analysis so as to obtain a globally optimum detection and tracking of objects.