A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine
Liang Dai, Yuesheng Zhu, Guibo Luo, Chao He, Hanchi Lin · Unmanned Systems · 2015
Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.