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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. For high-level Visual Convolutional neural networks trained on object recognition recognition problems, it is effective to use Features from the datasets to improve Tracking accuracy and robustness.

the hierarchical features from the recent advances in CNNs and the inference approach across multiple levels in clas-sical computer vision problems. For example, computing optical flow from the coarse levels of the image pyramid are efficient, but finer levels are required for obtaining an accurate and detailed flow field. A coarse-to ...

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