Performance of optical flow techniques
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Object Oriented Motion Estimation in Color Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust Salient Motion Detection with Complex Background for Real-Time Video Surveillance
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Continuous tracking within and across camera streams
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Motion estimation using a complex-valued wavelet transform
IEEE Transactions on Signal Processing
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
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This paper describes real-time computer vision algorithms for detection, identification, and tracking of moving targets in video streams generated by a moving airborne platform. Moving platforms cause instabilities in image acquisition due to factors such as disturbances and the ego-motion of the camera that distorts the actual motion of the moving targets. When the camera is mounted on a moving observer, the entire scene (background and targets) appears to be moving and the actual motion of the targets must be separated from the background motion. The motion of the airborne platform is modeled as affine transformation and its parameters are estimated using corresponding feature sets in consecutive images. After motion is compensated, the platform is considered as stationary and moving targets are detected accordingly. A number of tracking algorithms including particle filters, mean-shift, and connected component were implemented and compared. A cascaded boosted classifier with Haar wavelet feature extraction for moving target classification was developed and integrated with the recognition system that uses joint-feature spatial distribution. The integrated smart video surveillance system has been successfully tested using the Vivid Datasets provided by the Air Force Research Laboratory. The experimental results show that system can operate in real time and successfully detect, track, and identify multiple targets in the presence of partial occlusion.