Background Modeling for Segmentation of Video-Rate Stereo Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
International Journal of Robotics Research
On pedestrian detection and tracking in infrared videos
Pattern Recognition Letters
Taking mobile multi-object tracking to the next level: people, unknown objects, and carried items
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Fast stixel computation for fast pedestrian detection
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
A data-driven detection optimization framework
Neurocomputing
Robust object tracking in crowd dynamic scenes using explicit stereo depth
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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In this paper we describe a fully integrated system for detecting, localizing, and tracking pedestrians from a moving vehicle. The system can reliably detect upright pedestrians to a range of 40 m in lightly cluttered urban environments. The system uses range data from stereo vision to segment the scene into regions of interest, from which shape features are extracted and used to classify pedestrians. The regions are tracked using shape and appearance features. Tracking is used to temporally filter classifications to improve performance and to estimate the velocity of pedestrians for use in path planning. The end-to-end system runs at 5 Hz on 1,024 脙聴 768 imagery using a standard 2.4 GHz Intel Core 2 Quad processor, and has been integrated and tested on multiple ground vehicles and environments. We show performance on a diverse set of datasets with groundtruth in outdoor environments with varying degrees of pedestrian density and clutter. In highly cluttered urban environments, the detection rates are on a par with state-of-the-art but significantly slower systems.