Dr. Dan Hu Profile

Dr. Dan Hu

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Proceedings Article | 9 December 2015 Paper

Robust visual track using an ensemble cascade of convolutional neural networks

Dan Hu, Xingshe Zhou, Xiaohao Yu, Zhiqiang Hou

Proceedings Volume 9817, 98170W (2015) https://doi.org/10.1117/12.2228001

KEYWORDS: Convolutional neural networks, Optical tracking, Neural networks, Video, Visualization, Neurons, Performance modeling, Detection and tracking algorithms, Data modeling, Convolution

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Convolutional Neural Networks (CNN) have dramatically boosted the performance of various computer vision tasks except visual tracking due to the lack of training data. In this paper, we pre-train a deep CNN offline to classify the 1 million images from 256 classes with very leaky non-saturating neurons for training acceleration, which is transformed to a discriminative classifier by adding an additional classification layer. In addition, we propose a novel approach for combining increasingly our CNN classifiers in a “cascade” structure through a modification of the AdaBoost framework, and then transfer the selected discriminative features from the ensemble of CNN classifiers to the robust visual tracking task, by updating online to robustly discard the background regions from promising object-like region to cope with appearance changes of the target. Extensive experimental evaluations on an open tracker benchmark demonstrate outstanding performance of our tracker by improving tracking success rate and tracking precision on an average of 9.2% and 13.9% at least over other state-of-the-art trackers.

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