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CVPR
2010
IEEE
13 years 10 months ago
Learning Appearance in Virtual Scenarios for Pedestrian Detection
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers tra...
Francisco Marin Tur, David Vazquez, David Geronimo...
DAGM
2006
Springer
13 years 8 months ago
Cross-Articulation Learning for Robust Detection of Pedestrians
Recognizing categories of articulated objects in real-world scenarios is a challenging problem for today's vision algorithms. Due to the large appearance changes and intra-cla...
Edgar Seemann, Bernt Schiele
AAAI
2008
13 years 6 months ago
Multimodal People Detection and Tracking in Crowded Scenes
This paper presents a novel people detection and tracking method based on a multi-modal sensor fusion approach that utilizes 2D laser range and camera data. The data points in the...
Luciano Spinello, Rudolph Triebel, Roland Siegwart
CVPR
2011
IEEE
12 years 12 months ago
FlowBoost - Appearance Learning from Sparsely Annotated Video
We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists...
Karim Ali, Francois Fleuret, David Hasler
ECCV
2008
Springer
13 years 6 months ago
Multiple Instance Boost Using Graph Embedding Based Decision Stump for Pedestrian Detection
Pedestrian detection in still image should handle the large appearance and stance variations arising from the articulated structure, various clothing of human as well as viewpoints...
Junbiao Pang, Qingming Huang, Shuqiang Jiang