J
Jun-Wei Hsieh
Researcher at National Chiao Tung University
Publications - 161
Citations - 5602
Jun-Wei Hsieh is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Object detection & Feature extraction. The author has an hindex of 31, co-authored 142 publications receiving 3582 citations. Previous affiliations of Jun-Wei Hsieh include Advanced Technology Center & Moscow State University.
Papers
More filters
Proceedings ArticleDOI
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
TL;DR: Cross Stage Partial Network (CSPNet) as discussed by the authors integrates feature maps from the beginning and the end of a network stage to mitigate the problem of duplicate gradient information within network optimization.
Journal ArticleDOI
Automatic traffic surveillance system for vehicle tracking and classification
TL;DR: Experimental results show that the proposed automatic traffic surveillance system is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.
Journal ArticleDOI
Vehicle Detection Using Normalized Color and Edge Map
TL;DR: Zhang et al. as discussed by the authors proposed a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates, and three important features including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier.
Journal ArticleDOI
Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle Make and Model Recognition
TL;DR: A new symmetrical SURF descriptor is proposed to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation to detect vehicles from the road without using any motion features.
Journal ArticleDOI
Shadow elimination for effective moving object detection by Gaussian shadow modeling
TL;DR: This paper presents a novel approach for eliminating unexpected shadows from multiple pedestrians from a static and textured background using Gaussian shadow modeling, and demonstrates that approximately 94% of shadows can be successfully eliminated from the scene background.