W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
A Statistical Field Model for Pedestrian Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Field Model for Human Detection and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we propose a novel approach for detecting pedestrians from video sequence acquired with non-static camera. The proposed algorithm consists of three major components, including global motion estimation with motion-compensated frame subtraction, AdaBoost pedestrian detection, and temporal integration. The global motion estimation with frame subtraction can reduce the influence of the background pixels and improve the detection accuracy and efficiency. The simplified affine model is used to fit the global motion model from some reliable blocks by using the RANSAC robust estimation algorithm. After motion-compensated frame subtraction, the AdaBoost classifier is employed to detection pedestrians in a single frame. At last, the graph structure is applied to model the relationship of different detection windows in the temporal domain. Similar detected windows are grouped as the same clusters by using the optimal linking algorithm. The missed detection windows will be recovered from the object clustering results. Finally, we show the experimental results by using the proposed pedestrian detection algorithm on some real video sequences to demonstrate its high detection accuracy and low false alarm rate.