Compressive tracking with locality sensitive histograms features

Currently, Compressive Tracking (CT) method has drawn great attention because of its high efficiency. However, it cannot well deal with some appearance variations due to its limitations of feature expression and it only uses a fixed parameter to update the appearance model. In order to handle such matters, we propose an adaptive CT method that combines the predicted target position with CT based on Locality Sensitive Histograms (LSH) features. Our method significantly improves CT in four aspects. First, the efficient illumination invariant features extracted based on LSH are used to represent an effective appearance model that is robust to illumination changes. Second, the color attributes tracker is adopted to predict the target position for re-building the new weighted discriminant function which brings in the color information to make up for the inadequacy of Haar-like characteristics. Third, a new model update mechanism is proposed to preserve the stable features while avoid the noisy appearance variations during tracking. Fourth, a trajectory rectification method is employed to refine the tracking location when possible inaccurate tracking occurs. Finally, we show that our tracker achieves state-of-the-art performance in a comprehensive evaluation over 47 challenging color sequences.

Authors

• Sixian Chan 0001
• Xiaolong Zhou 0001
• Zhuo Zhang 0012
• Shengyong Chen

Keywords

• Target tracking
• Feature extraction
• Image color analysis
• Histograms
• Computed tomography
• Prediction algorithms
• Lighting
• Histogram
• Compressive Tracking
• Linear Discriminant Analysis
• Efficient Feature
• Color Information
• Illumination Changes
• Appearance Variations
• Location Tracking
• Comparable Results
• Positive Samples
• Negative Samples
• Adaptive Algorithm
• Maximum Response
• Bounding Box
• Tracking Accuracy
• Tracking Algorithm
• Random Matrix
• Tracking Results
• Motion Blur
• Illumination Variations
• Target Frame
• Standard Deviation Of Features
• Background Clutter
• Adaptive Tracking
• Random Projection
• In-plane Rotation
• High-dimensional Feature Vector
• Integral Image
• Object Appearance
• Current Frame