Hierarchical Convolutional Features for Visual Tracking Chao Ma, SJTU Jia-Bin Huang, UIUC Xiaokang Yang, SJTU Ming-Hsuan Yang, UC Merced
Hierarchical Convolutional Features for Visual Tracking Chao Ma，SJTU Jia-Bin Huang，UIUC Xiaokang Yang，SJTU Ming-Hsuan Yang，UC Merced
Hierarchical Convolutional features for visual racking What is visual tracking? How to do it? What is the novel point of this paper
Hierarchical Convolutional Features for Visual Tracking • What is visual tracking？ • How to do it? • What is the Novel point of this paper?
Visual Tracking A typical scenario of visual tracking is to track an unknown target object, specified by a bounding box in the first frame #050 #080 #02
Visual Tracking • A typical scenario of visual tracking is to track an unknown target object, specified by a bounding box in the first frame
Visual tracking ng Method Tracking by binary Classifiers Visual tracking can be posed as a repeated detection problem in a local window. For each frame, a set of positive and negative training samples are collected for incrementally learning a discriminative classifier to separate a target from its backgrounds Sampling ambiguity Tracking by Correlation Filters Tracking methods based on correlation filters regress all the circular-shifted versions of input features to a target gaussian function and thus no hard-thresholded samples of target appearance are needed. Tracking by CNNs Visual representations are of great importance for object tracking
Visual Tracking Method • Tracking by Binary Classifiers. • Visual tracking can be posed as a repeated detection problem in a local window. For each frame, a set of positive and negative training samples are collected for incrementally learning a discriminative classifier to separate a target from its backgrounds. • Sampling ambiguity • Tracking by Correlation Filters. • Tracking methods based on correlation filters regress all the circular-shifted versions of input features to a target Gaussian function and thus no hard-thresholded samples of target appearance are needed. • Tracking by CNNs • Visual representations are of great importance for object tracking
Chao mas Work Learn correlation filters over multi-dimensional features in a way similar to existing methods The main differences lie in the use of learned CNN features rather than hand-crafted features Former CNn trackers all rely on positive and negative training samples and only exploit the features from the last layer. In contrast, our approach builds on adaptive correlation filters which regress the dense, circularly shifted samples with soft labels and effectivel alleviate sampling ambiguity
Chao Ma’s Work • Learn correlation filters over multi-dimensional features in a way similar to existing methods. The main differences lie in the use of learned CNN features rather than hand-crafted features • Former CNN trackers all rely on positive and negative training samples and only exploit the features from the last layer. In contrast, our approach builds on adaptive correlation filters which regress the dense, circularly shifted samples with soft labels and effectively alleviate sampling ambiguity