CRNet: Corner Recognition from Trajectories Based on Convolutional and Recurrent Neural Networks

Corner recognition plays an important role in trajectory-route matching for fingerprint crowdsourcing in indoor positioning systems. However, the pose diversity and fake corner problem in most practical scenarios could much degrade the recognition performance. This paper deals with the two problems from a deep learning perspective. We design the CRNet consisting of convolutional and recurrent neural networks, which can recognize a corner from pedestrian movement trajectories directly from raw sensor measurements. Field experiments have validated its effectiveness in terms of much improved precision, recall and F1-measure compared with the state-of-the-art schemes. As the CRNet does not require manual feature extraction, we envision its wide applications in practical corner recognition scenarios.