V
Vitaly Ablavsky
Researcher at University of Washington
Publications - 45
Citations - 628
Vitaly Ablavsky is an academic researcher from University of Washington. The author has contributed to research in topics: Video tracking & Object detection. The author has an hindex of 12, co-authored 43 publications receiving 463 citations. Previous affiliations of Vitaly Ablavsky include Charles River Laboratories & Istituto Italiano di Tecnologia.
Papers
More filters
Proceedings ArticleDOI
The 5th AI City Challenge
Milind Naphade,Shuo Wang,David C. Anastasiu,Zheng Tang,Ming-Ching Chang,Xiaodong Yang,Yue Yao,Liang Zheng,Pranamesh Chakraborty,Anuj Sharma,Qi Feng,Vitaly Ablavsky,Stan Sclaroff +12 more
TL;DR: The fifth AI City Challenge as mentioned in this paper attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks: Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency.
Journal ArticleDOI
Learning a Family of Detectors via Multiplicative Kernels
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.
Journal ArticleDOI
Take your eyes off the ball: Improving ball-tracking by focusing on team play
TL;DR: This paper proposes a novel approach to addressing the issue of accurate video-based ball tracking in team sports by formulating the tracking in terms of deciding which player, if any, is in possession of the ball at any given time.
Proceedings ArticleDOI
Optimal search for a moving target: a geometric approach
TL;DR: A divide-andconquer geometric approach for constructing optimal search paths for arbitrarily-shaped regions of interest that is both generalizable to multiple search agents and extensible in that additional real-life search requirements can be incorporated into the existing framework.
Proceedings ArticleDOI
Automatic feature selection with applications to script identification of degraded documents
Vitaly Ablavsky,M.R. Stevens +1 more
TL;DR: This work presents an approach that applies a large pool of image features to a small training sample and uses subsetfeature selection techniques to automatically select a subset with the most discriminating power once a preset likelihood threshold has been reached.