Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection with Kernel Class Separability
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid object tracking in complex scenes
Pattern Recognition Letters
Object tracking in image sequences using point features
Pattern Recognition
Object tracking with particle filter using color information
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Feature selection for reliable tracking using template matching
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Two denoising methods by wavelet transform
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
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours for tracking distributions
IEEE Transactions on Image Processing
Monocular precrash vehicle detection: features and classifiers
IEEE Transactions on Image Processing
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The traditional algorithms often cannot track moving objects accurately in real time. In order to overcome the problem, this paper proposes a new method based on wavelet features for target tracking. Specifically, according to the previous information obtained by particle filter, the possible location of the target in the frame is predicted. Multi-scale two-dimensional discrete wavelet is used to characterize the possible target regions. Then the means and variances of the decomposed image are computed. Finally, Principal Component Analysis (PCA) is used to build an effective subspace. Tracking is achieved by measuring the similarity function between the target and the image regions. In addition, to combat complex background and occlusion, the characterization vector is updated based on the similarity between the object model and candidate object regions. The experimental results demonstrate that the proposed algorithm is robust and can significantly improve the speed and accuracy of target tracking.