CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Tracking groups of people with a multi-model hypothesis tracker
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Particle filter-based visual tracking with a first order dynamic model and uncertainty adaptation
Computer Vision and Image Understanding
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Vehicle detection and tracking in airborne videos by multi-motion layer analysis
Machine Vision and Applications
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Tracking in airborne circumstances is receiving more and more attention from researchers, and it has become one of the most important components in video surveillance for its advantage of better mobility, larger surveillance scope and so on. However, airborne vehicle tracking is very challenging due to the factors such as platform motion, scene complexity, etc. In this paper, to address these problems, a new framework based on Kanade-Lucas-Tomasi (KLT) features and particle filter is proposed. KLT features are tracked throughout the video sequence. At the beginning of video tracking, a strategy based on motion consistence with RANSAC is utilized to separate background KLT features. The grouping of background features helps estimate the ego motion of the platform and the estimation is then incorporated into the prediction step in particle filter. Color similarity and Hu moments are used in the measurement model to assign the weights of particles. Our experimental results demonstrated that the proposed method outperformed the other tracking methods.