Performance of optical flow techniques
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Quasi-Random Sampling for Condensation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
ACM Computing Surveys (CSUR)
A revaluation of frame difference in fast and robust motion detection
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Visual saliency based object tracking
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Abrupt motion is a significant challenge that commonly causes traditional tracking methods to fail. This paper presents an improved visual saliency model and integrates it to a particle filter tracker to solve this problem. Once the target is lost, our algorithm recovers tracking by detecting the target region from salient regions, which are obtained in the saliency map of current frame. In addition, to strengthen the saliency of target region, the target model is used as a prior knowledge to calculate a weight set which is utilized to construct our improved saliency map adaptively. Furthermore, we adopt the covariance descriptor as the appearance model to describe the object more accurately. Compared with several other tracking algorithms, the experimental results demonstrate that our method is more robust in dealing with various types of abrupt motion scenarios.