DenseNet for Dense Flow
This work addresses the need for faster optical flow estimation in real-time video analysis, representing an incremental improvement by adapting an existing architecture to a specific domain.
The paper tackled the problem of slow optical flow estimation by proposing DenseNet, a deep architecture with shortcut connections, for unsupervised learning of motion estimation, achieving better performance than other CNN architectures on three standard benchmarks.
Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision. We extend current DenseNet to a fully convolutional network to learn motion estimation in an unsupervised manner. Evaluation results on three standard benchmarks demonstrate that DenseNet is a better fit than other widely adopted CNN architectures for optical flow estimation.