Video Tracking Using Learned Hierarchical Features

Abstract

In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object. As a result, the learned hierarchical features can be robust to both complicated motion transformations and appearance changes of target objects. We integrate our feature learning algorithm into three tracking methods. Experimental results demonstrate that significant improvement can be achieved using our learned hierarchical features, especially on video sequences with complicated motion transformations.

Robust visual tracking via multiscale deep sparse networks

“…Li et al 23 applied a single-CNN on visual tracking tasks without pretraining and combined it with multiple image cues to improve the tracking success rate. Wang et al 24 used hierarchical features for tracking by training a two-layer CNN on an auxiliary dataset and gained a good result in complicated tracking situations. Zhang et al 25 proposed a convolutional network-based tracker (CNT), which combined the local structure feature and global geometric information of tracking targets and attained a state-of-the-art performance.…”

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

“…Wang and Yeung [34] propose an autoencoder based tracking method. Instead of using unrelated images for pretraining, Wang et al [57] propose a tracking method which prelearns features robust to diverse motion patterns from auxiliary video sequences. However, they only evaluate the method on 10 video sequences.…”

Feature Adaptive Correlation Tracking

“…With the development of deep learning technology, convolutional neural networks (CNNs) have demonstrated their outstanding representation power in a wide range of computer vision applications [11]. And some tracking algorithms using the representations from CNNs have been proposed [12], [13], [22]. In addition, Ma et al [14] utilized convolutional features and learned correlation filters on each CNN layer without re-training.…”