CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A General Framework for Combining Visual Trackers --- The "Black Boxes" Approach
International Journal of Computer Vision
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
ACM Computing Surveys (CSUR)
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Minimum error bounded efficient $/ell _1$ tracker with occlusion detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Online Bayesian estimation of transition probabilities for Markovian jump systems
IEEE Transactions on Signal Processing
Automatic parameter adaptation for multi-object tracking
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an object's appearance. To fuse these independent trackers, we propose two configurations, tracker selection and interaction. The tracker interaction is achieved based on a transition probability matrix (TPM) in a probabilistic manner. The tracker selection extracts one tracking result from among multiple tracker outputs by choosing the tracker that has the highest tracker probability. According to various changes in an object's appearance, the TPM and tracker probability are updated in a recursive Bayesian form by evaluating each tracker's reliability, which is measured by a robust tracker likelihood function (TLF). When the tracking in each frame is completed, the estimated object's state is obtained and fed into the reference update via the proposed learning strategy, which retains the robustness and adaptability of the TLF and multiple trackers. The experimental results demonstrate that our proposed method is robust in various benchmark scenarios.