Self Paced Densely Connected Convolutional Neural Network for Visual Tracking
Convolutional neural networks (CNNs) have achieved surprising results in visual tracking. To address the model drift problem, we propose a novel self paced densely connected convolutional neural netwrok (SPDCT) to distinguish the reliable data from noisy and confusing data. In the proposed model, each sample is given a weight, which is estimated by SPDCT to indicate the reliability of the sample. The self paced learning framework is then integrated into the online update phase to improve the robustness of CNNs. In order to determine the pace parameter of self paced learning effectively, we propose an adaptive method based on the number of training samples. Meanwhile, with the aim of facilitating the representation power of the features, we enhance the feature reuse and the information flow by applying the densely connected learning. Extensive experimental results demonstrate competing performance of the proposed tracker over a number of state of the art algorithms.
PRCV