Detecting Independent Motion: The Statistics of Temporal Continuity
IEEE Transactions on Pattern Analysis and Machine Intelligence
MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences
Machine Vision and Applications
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Moving Objects in Airborne Forward Looking Infra-Red Sequences
CVBVS '99 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Probabilistic Notion of Correspondence and the Epipolar Constraint
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Mean Shift Based Target Tracking in FLIR Imagery via Adaptive Prediction of Initial Searching Points
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 01
Infrared Target Tracking with AM-FM Consistency Checks
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
Aerial moving target detection based on motion vector field analysis
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Vehicle tracking based on image alignment in aerial videos
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A probabilistic framework for correspondence and egomotion
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Efficient multi-feature PSO for fast gray level object-tracking
Applied Soft Computing
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A novel strategy for object tracking in aerial imagery is presented, which is able to deal with complex situations where the camera ego-motion cannot be reliably estimated due to the aperture problem (related to low structured scenes), the strong ego-motion, and/or the presence of independent moving objects. The proposed algorithm is based on a complex modeling of the dynamic information, which simulates both the object and the camera dynamics to predict the putative object locations. In this model, the camera dynamics is probabilistically formulated as a weighted set of affine transformations that represent possible camera egomotions. This dynamic model is used in a Particle Filter framework to distinguish the actual object location among the multiple candidates, that result from complex cluttered backgrounds, and the presence of several moving objects. The proposed strategy has been tested with the aerial FLIR AMCOM dataset, and its performance has been also compared with other tracking techniques to demonstrate its efficiency.