Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Deformable Objects in the Plane Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Visual attention based motion object detection and trajectory tracking
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Salient object detection in videos by optimal spatio-temporal path discovery
Proceedings of the 21st ACM international conference on Multimedia
Abrupt motion tracking using a visual saliency embedded particle filter
Pattern Recognition
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This paper presents a novel method of on-line object tracking with the static and motion saliency features extracted from the video frames locally, regionally and globally. When detecting the salient object, the saliency features are effectively combined in Conditional Random Field (CRF). Then Particle Filter is used when tracking the detected object. Like the attention shifting mechanism of human vision, when the object being tracked disappears, our tracking algorithm can change its target to other object automatically even without re-detection. And different from many other existing tracking methods, our algorithm has little dependence on the surface appearance of the object, so it can detect any category of objects as long as they are salient, and the tracking is robust to the change of global illumination and object shape. Experiments on video clips of various objects show the reliable results of our algorithm.