An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Image Stabilization by Features Tracking
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Image Stabilization and Mosaicking Using the Overlapped Basis Optical Flow Field
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Using Particles to Track Varying Numbers of Interacting People
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Evolutionary approach for semantic-based query sampling in large-scale information sources
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Human pose tracking using multi-level structured models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
| Hi-index | 0.00 |
Robust detection and tracking of pedestrians in image sequences are essential for many vision applications. In this paper, we propose a method to detect and track multiple pedestrians using motion, color information and the AdaBoost algorithm. Our approach detects pedestrians in a walking pose from a single camera on a mobile or stationary system. In the case of mobile systems, ego-motion of the camera is compensated for by corresponding feature sets. The region of interest is calculated by the difference image between two consecutive images using the compensated image. Pedestrian detector is learned by boosting a number of weak classifiers which are based on Histogram of Oriented Gradient (HOG) features. Pedestrians are tracked by block matching method using color information. Our tracking system can track pedestrians with possibly partial occlusions and without misses using information stored in advance even after occlusion is ended. The proposed approach has been tested on a number of image sequences, and was shown to detect and track multiple pedestrians very well.