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
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Multimedia surveillance systems
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Feature-level Fusion for Object Segmentation using Mutual Information
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Multimedia surveillance: content-based retrieval with multicamera people tracking
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
Machine Vision and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Framework for Evaluating Stereo-Based Pedestrian Detection Techniques
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.01 |
Contextual information is vital for the robust extraction of semantic information in automated surveillance systems. We have developed a scene independent framework for the detection of events in which we provide 2D and 3D contextual data for the scene under surveillance via a novel fast and convenient interface tool. In addition, the proposed framework illustrates the use of integral images, not only for detection, as with the classic Viola-Jones object detector, but also for efficient tracking. Finally, we provide a quantitative assessment of the performance of the proposed system in a number of physical locations via groundtruthed datasets.