Multiple vehicle tracking in surveillance videos

Authors:
Yun Zhai;Phillip Berkowitz;Andrew Miller;Khurram Shafique;Aniket Vartak;Brandyn White;Mubarak Shah
Affiliations:
Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida;Computer Vision Laboratory, School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida
Venue:
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Year:
2006

Citing 15
Cited 1

Finding Trajectories of Feature Points in a Monocular Image Sequence

IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence

CVGIP: Image Understanding
Image difference threshold strategies and shadow detection

BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices

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
Resolving Motion Correspondence for Densely Moving Points

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Comparing Images under Variable Illumination

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information

MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Supervised Learning Framework for Generic Object Detection in Images

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scene monitoring with a forest of cooperative sensors

Scene monitoring with a forest of cooperative sensors

Objective Evaluation of Pedestrian and Vehicle Tracking on the CLEAR Surveillance Dataset

Multimodal Technologies for Perception of Humans

Quantified Score

Hi-index	0.02

Visualization

Abstract

In this paper, we present KNIGHT, a Windows-based stand-alone object detection, tracking and classification software, which is built upon Microsoft Windows technologies. The object detection component assumes stationary background settings and models background pixel values using Mixture of Gaussians. Gradient-based background subtraction is used to handle scenarios of sudden illumination change. Connected-component algorithm is applied to detected foreground pixels for finding object-level moving blobs. The foreground objects are further tracked based on a pixel-voting technique with the occlusion and entry/exit reasonings. Motion correspondences are established using the color, size, spatial and motion information of objects. We have proposed a texture-based descriptor to classify moving objects into two groups: vehicles and persons. In this component, feature descriptors are computed from image patches, which are partitioned by concentric squares. SVM is used to build the object classifier. The system has been used in the VACE-CLEAR evaluation forum for the vehicle tracking task. Corresponding system performance is presented in this paper.