Face tracking with convolutional neural network heat-map
Nhu-Tai Do, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, In Seop Na · 2018
In this paper, we apply a heat-map approach for human face tracking. We utilize the heat-map extracted from the convolutional neural networks (CNN) for face / non-face classification problem. The CNN architecture we build is a shallow network to extract information that is meaningful in locating an object. In addition, we made many CNNs with changes in pool-size of the last layer to obtain a well-defined heat-map. Experiments in the Visual Tracking Object dataset show that the results of the method are very encouraging. This shows the effectiveness of our proposed method.