Pedestrian navigation activity recognition (PNAR) has a significant impact on positioning and tracking performance. Smartphone-based PNAR utilizes measurements from sensors embedded in smartphone to identify pedestrian motion mode and smartphone usage mode. Compared with other PNAR technologies, smartphone-based PNAR has the advantage of autonomy and practicality. Though various PNAR recognition methods based on smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods, and solutions of PNAR are relatively less so far in the literature. This work aims to present an elaborated, timely, and valuable survey for different smartphone-based PNAR techniques, including sensor calibration, pedestrian modeling, pedestrian motion and smartphone usage modes definition, workflow, state-of-the-art research methods, public datasets, performance evaluation metrics, and relative competitions. Finally, applications, current challenges and future research trends are discussed. The work propels a better understanding of existing navigation activity recognition methods. It is helpful for researchers to further design PNAR methods, and develop more accurate and robust pedestrian positioning and navigation systems.
Intelligent manufacturing is a challenging and compelling topic in Industry 4.0. Many computer vision (CV)-based applications have attracted widespread interest from researchers and industries around the world. However, it is difficult to integrate visual recognition algorithms with industrial control systems. The low-level devices are controlled by traditional programmable logic controllers (PLCs) that cannot realize data communication due to different industrial control protocols. In this article, we develop a multi-crane visual sorting system with cloud PLCs in a 5G environment, in which deep convolutional neural network (CNN)-based character recognition and dynamic scheduling are designed for materials in intelligent manufacturing. First, an YOLOv5-based algorithm is applied to locate the position of objects on the conveyor belt. Then, we propose a Chinese character recognition network (CCRNet) to significantly recognize each object from the original image. The position, type, and timestamp of each object are sent to cloud PLCs that are virtualized in the cloud to replace the function of traditional PLCs in the terminal. After that, we propose a dynamic scheduling method to sort the materials in minimum time. Finally, we establish a real experimental platform of a multi-crane visual sorting system to verify the performance of the proposed methods.
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