Ashwin Thangali

TL;DR: The ASL lexicon video dataset is introduced, a large and expanding public dataset containing video sequences of thousands of distinct ASL signs, as well as annotations of those sequences, including start/end frames and class label of every sign.

TL;DR: A novel framework for multiple object tracking in which the problems of object detection and data association are expressed by a single objective function, which follows the Lagrange dual decomposition strategy.

TL;DR: This work shows that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions.

TL;DR: An example computer vision application that leverages the ASLLVD is reported: the formulation employs a HandShapes Bayesian Network (HSBN), which models the transition probabilities between start and end handshapes in monomorphemic lexical signs.

TL;DR: In this work, linguistic constraints on the relationship between start and end handshapes are leveraged to improve handshape recognition accuracy, and a Bayesian network formulation is proposed for learning and exploiting these constraints, while taking into consideration inter-signer variations in the production of particular handsh shapes.