Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Efficient Annotation of Vesicle Dynamics Video Microscopy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Brief paper: A maximum-likelihood Kalman filter for switching discrete-time linear systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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Combing image processing technique and the probabilistic data association (PDA) motion model, we develop a novel framework to solve the problem of object tracking for non-electromechanical system with overwhelming noise background. The new model has two advantages: (1) By integrating the statistical motion model, the movement of object in many non-electromechanical systems could be more precisely simulated than existing ones. (2) Because of the adoption of a global search for optimal model parameters, the proposed model is better to track objects in high noise environment, comparing with other methods that rely on consecutive frames differentiating. We derive the expectation-maximization (EM) algorithm within the proposed model. Its usefulness is demonstrated with both synthesized data and image data set. Model Stability is introduced to quantify the usefulness of the model.