As surveillance cameras have become ubiquitous in daily life, these widely deployed cameras deliver an opportunity to localize and navigate people, especially in indoor environment where the ability of Global Position System (GPS) is limited. Thus, this paper aims to leverage monocular surveillance cameras to locate people in the physical real world. We propose a Surveillance Cameras-based System (SCS) for indoor localization. This system detects people in video images and use visual positioning techniques to extract features to locate and track people in the real world with 6-DoF pose. We design a real-time people detection algorithm, especially for a surveillance camera that is an edge device. Besides, we design a visual-based people localization algorithm. We implement SCS on an Ubuntu server and conduct extensive evaluation. The overall performance of SCS shows that the average absolute trajectory error and relative trajectory error of SCS is 0.67m and 0.29m, respectively. The average relative rotation error of SCS is less than 15º.
KEYWORDS: Education and training, Video, Pose estimation, Data acquisition, Tunable filters, Image processing, Cameras, Bone, RGB color model, Video processing
The full coverage of electronic products promotes the prevalence of human-computer interaction systems, especially in sports and fitness. Excellent human-computer interaction technology can bring high-quality experiences to human beings. However, the previous research focuses on the tracking of the wearable device and the radio frequency signal to the body part. This article will launch a lightweight, portable yoga private education system that can be mainly in line with coaches. A smartphone can realize real-time tracking of user body motion, combining the evaluation model feedback and valuable guidance. We predict the composition of the limb occlusion by the algorithm and use the segmentation to normalize the accurate calibration of the bone node. Improve the accuracy and robustness of the system while helping users achieve freedom and practical yoga training. The experimental results show that the system can provide meaningful feedback to the user in real-time by accurately assessing the user’s yoga action, high accuracy.
Wireless sensor technology can effectively sense the impact of the human body on wireless links, which is conducive to promoting the evolution of wireless networks to intelligent sensor networks. It has good application prospects in bright space, security, rescue, human-computer interaction, etc. Motion recognition based on wireless sensing has recently been a research hotspot. Passive RFID tags are battery-free, portable, and have good privacy protection. They can be transmitted in a non-line-of-sight range. Because of these advantages, this paper uses a deep convolution neural network to classify activities based on RFID Doppler shift data. We designed a complete yoga movement recognition and evaluation system to evaluate the whole design, taking 12 everyday basic yoga movements as examples. We designed and implemented the prototype of our scheme using an Inpinj R420 reader and RFID tag. We invited 30 volunteers to collect data and conduct comprehensive experiments. The experimental results show that the average recognition accuracy of the essential yoga activity recognition we designed is 93.20%. It can also be applied to recognizing other activities and has broad application prospects.
Many studies have shown that wireless sensing would be a promising method for liquid identification, but existing methods still have limitations for fine-grained liquid sensing. In this paper, we propose a liquid identification method based on multiple transceiver pairs, which effectively improves the sensing resolution of liquid identification. We implement our method with a commercially FMCW millimeter wave radar and evaluate its performance. Our result shows that for concentrations as low as 0.5% in alcohol solutions, our method can achieve an accuracy of more than 95%.
KEYWORDS: Signal detection, Ultrasonics, Defense and security, Signal attenuation, Accelerometers, Frequency response, Signal processing, Environmental sensing, Detection and tracking algorithms, Design and modelling
Inaudible attack has brought growing concerns over security of voice assistants. With a well-designed inaudible signal, an adversary can force the voice assistant to execute commands inaudibly like “Siri, open the door”. It is challenging to defend against ultrasonic attacks without modifying the hardware. In this paper, we proposed a light-weight system named IMUSHIELD to defend voice assistant against inaudible attack. By comparing the different response of signal from microphone and inertial measurement units (IMUs) to different frequencies on smartphones. IMUSHIELD is able to detect the attacks without modifying the hardware. We have prototyped our method on a number of smartphones and test the performance of IMUSHIELD comprehensively in the real world, the result shows that our average detection accuracy exceeded 90%.
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