M
Ming-Hsuan Yang
Researcher at University of California, Merced
Publications - 635
Citations - 96082
Ming-Hsuan Yang is an academic researcher from University of California, Merced. The author has contributed to research in topics: Convolutional neural network & Video tracking. The author has an hindex of 127, co-authored 635 publications receiving 75091 citations. Previous affiliations of Ming-Hsuan Yang include University of California, Irvine & University of California.
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Journal ArticleDOI
Detecting faces in images: a survey
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Proceedings ArticleDOI
Online Object Tracking: A Benchmark
TL;DR: Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field.
Journal ArticleDOI
Incremental Learning for Robust Visual Tracking
TL;DR: A tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target, and includes a method for correctly updating the sample mean and a forgetting factor to ensure less modeling power is expended fitting older observations.
Journal ArticleDOI
Object Tracking Benchmark
TL;DR: An extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria is carried out to identify effective approaches for robust tracking and provide potential future research directions in this field.
Proceedings ArticleDOI
Saliency Detection via Graph-Based Manifold Ranking
TL;DR: This work considers both foreground and background cues in a different way and ranks the similarity of the image elements with foreground cues or background cues via graph-based manifold ranking, defined based on their relevances to the given seeds or queries.