Kalman filtering: theory and practice
Kalman filtering: theory and practice
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Localisation and Recognition of Road Vehicles
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A DSP-Based Real Time Contour Tracking System
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Depth and motion discontinuities
Depth and motion discontinuities
Boundary Based Corner Detecion and Localization Using New 'Cornerity' Index: A Robust Approach
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Model-Based Vehicle Segmentation Method for Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Analysis & Applications
A feature-based tracking algorithm for vehicles in intersections
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Initialization of Model-Based Vehicle Tracking in Video Sequences of Inner-City Intersections
International Journal of Computer Vision
Separation of Foreground Text from Complex Background in Color Document Images
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A new approach for vehicle detection in congested traffic scenes based on strong shadow segmentation
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Counting Pedestrians in Video Sequences Using Trajectory Clustering
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present a novel approach for detection of moving vehicles in traffic videos. We propose a feature-based (corner-based) tracking to track and classify moving vehicles from the extracted ghost or cast shadow. The corner points of the vehicles are detected, labeled and grouped to generate a unique label per vehicle. This approach is able to deal with different types of deformations on the shape of the vehicles due to changes in size, direction and viewpoint. Also, the proposed method is totally free from motion estimation. To demonstrate the robustness and accuracy of our system, the results of the experiments are conducted on traffic videos including different complex background, illumination, motion, camera position, clutter and direction of the vehicles taken from outdoor boulevards and city roads. We detect moving vehicles on an average of 98.8% in a scene. The results show the robustness of our proposed algorithm.