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Dawei Du

Researcher at State University of New York System

Publications -  80
Citations -  4393

Dawei Du is an academic researcher from State University of New York System. The author has contributed to research in topics: Video tracking & Object detection. The author has an hindex of 22, co-authored 71 publications receiving 2797 citations. Previous affiliations of Dawei Du include University at Albany, SUNY & Chinese Academy of Sciences.

Papers
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Book ChapterDOI

The Visual Object Tracking VOT2016 Challenge Results

Matej Kristan, +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Proceedings ArticleDOI

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, +104 more
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Book ChapterDOI

The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking

TL;DR: In this article, a new unconstrained UAV benchmark dataset is proposed for object detection, single object tracking, and multiple object tracking with new level challenges, including high density, small object, and camera motion, and a detailed quantitative study is performed using most recent state-of-the-art algorithms for each task.
Journal ArticleDOI

UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

TL;DR: This work performs a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
Posted Content

UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

TL;DR: In this paper, the authors performed a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.