Development of real-time indoor human tracking system using LoRa technology

<p>Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios.</p>

Human Tracking of a Crawler Robot in Climbing Stairs

There are many reports of secondary damage to crews during firefighting operations. One way to support and enhance their activities is to get robots to track them and carry supplies. In this paper, we propose a localization method for stairs that includes scene detection. The proposed method allows a robot to track a person across stairs. First, the scene detection autonomously detects that the person is climbing the stairs. Then, the linear model representing the first step of the staircase is combined with the person’s trajectory for localization. The method uses omnidirectional imaging and point clouds, and the localization and scene detection are available from any posture around the stairs. Finally, using the localization result, the robot automatically navigates to a posture where it can climb the stairs. Verification confirmed the accuracy and real-time capability of the method and demonstrated that the actual crawler robot autonomously chooses a posture that is ready for climbing.

Human Segmentation and Tracking Survey on Masks for MADS Dataset

Human segmentation and tracking often use the outcome of person detection in the video. Thus, the results of segmentation and tracking depend heavily on human detection results in the video. With the advent of Convolutional Neural Networks (CNNs), there are excellent results in this field. Segmentation and tracking of the person in the video have significant applications in monitoring and estimating human pose in 2D images and 3D space. In this paper, we performed a survey of many studies, methods, datasets, and results for human segmentation and tracking in video. We also touch upon detecting persons as it affects the results of human segmentation and human tracking. The survey is performed in great detail up to source code paths. The MADS (Martial Arts, Dancing and Sports) dataset comprises fast and complex activities. It has been published for the task of estimating human posture. However, before determining the human pose, the person needs to be detected as a segment in the video. Moreover, in the paper, we publish a mask dataset to evaluate the segmentation and tracking of people in the video. In our MASK MADS dataset, we have prepared 28 k mask images. We also evaluated the MADS dataset for segmenting and tracking people in the video with many recently published CNNs methods.