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Hierarchical Convolutional Features for Visual Tracking

Hierarchical Convolutional Features for Visual Tracking Chao Ma Jia-Bin Huang Xiaokang Yang Ming-Hsuan Yang SJTU UIUC SJTU UC Merced Abstract around the estimated target location to incrementally learn a classifier over Features extracted from a CNN. Two issues Visual object Tracking is challenging as target objects of- ensue with such approaches. The first issue lies in the use ten undergo significant appearance changes caused by de- of neural networks as an online classifier following recent formation, abrupt motion, background clutter and occlu- object recognition algorithms, where only the outputs of the sion. In this paper, we exploit Features extracted from deep last layer are used to represent targets.

lutional layer and coarse-to-fine search the multi-level cor-relation response maps to infer the location of targets. Cropped Search Window Conv3 Conv4 Conv5 Tracking Output (1) Position in last frame Estimated position w(2) w(3) Figure 3. Main steps of the proposed algorithm. Given an image, we first crop the search window centered at the ...

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