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Andreas Mogelmose

Researcher at Aalborg University

Publications -  31
Citations -  1581

Andreas Mogelmose is an academic researcher from Aalborg University. The author has contributed to research in topics: Traffic sign recognition & Pedestrian detection. The author has an hindex of 16, co-authored 29 publications receiving 1276 citations. Previous affiliations of Andreas Mogelmose include University of California, San Diego & MediaTech Institute.

Papers
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Journal ArticleDOI

Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

Andreas Mogelmose, +2 more
- 01 Dec 2012 - 
TL;DR: A survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance and discussing future directions of TSR research, including the integration of context and localization.
Journal ArticleDOI

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

Morten Bornø Jensen, +4 more
- 03 Feb 2016 - 
TL;DR: The evaluation of TLR systems is studied and discussed in depth, and a common evaluation procedure is proposed, which will strengthen evaluation and ease comparison, and an extensive public data set based on footage from U.S. roads is published.
Journal ArticleDOI

Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

Antonio Prioletti, +5 more
- 01 Sep 2013 - 
TL;DR: The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype and offers high performance in terms of detection rate, false positives per hour, and frame rate.
Proceedings ArticleDOI

Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset

Mark Philip Philipsen, +4 more
TL;DR: An extensive public database is collected based on footage from US roads, which shows that the learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.
Journal ArticleDOI

Multi-modal RGB---Depth---Thermal Human Body Segmentation

Cristina Palmero, +5 more
- 01 Jun 2016 - 
TL;DR: The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations.