IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive Fusion Architecture for Target Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Pedestrian detection by means of far-infrared stereo vision
Computer Vision and Image Understanding
Pedestrian detection and tracking in infrared imagery using shape and appearance
Computer Vision and Image Understanding
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
Machine Vision and Applications
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle
International Journal of Robotics Research
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel multiplex cascade classifier for pedestrian detection
Pattern Recognition Letters
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This article presents an approach for pedestrian detection and tracking from infrared imagery. The GMM background model is first deployed to separate the foreground candidates from background, then a shape describer is introduced to construct the feature vector for pedestrian candidates, and a SVM classifier is trained based on datasets generated from infrared images or manually. After detecting the pedestrian based on the SVM classifier, a multi-cues fusing algorithm is provided to facilitate the task of pedestrian tracking using both edge feature and intensity feature under the particle filter framework. Experimental results with various Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.