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Among all the road accidents, speeding is the most deadly factor. To reduce speeding, it is essential to devise efficient schemes for ubiquitous speed monitoring. Traditional approaches either suffers from using special equipment(e.g., radar speed gun) or special deployment(e.g., position-fixed cameras). In this article, we propose SpeedTalker, a mobile phone-based approach to perform speed detection on automobiles. By leveraging the built-in microphones and camera from the mobile phone, SpeedTalker estimates the automobile speed by passively sensing the acoustic and image signals. We propose an integrated solution to effectively estimate the automobile’s speed based on COTS devices, and provide a platform for every pedestrian to help report the speeding event of automobiles. Specifically, we use the time difference of arrivals (TDOA) model based on acoustic signals to figure out the candidate trajectories of automobile, and use the pin-hole model based on image frames to figure out the vertical distance between the user’s position and the automobile’s trajectory, thus to estimate the unique trajectory. Combined with the time stamp of the trajectory, the automobile speed can be estimated. Besides, we propose a method to effectively mitigate the influence of the movement jitters of mobile phone. We implemented a system prototype for SpeedTalker and estimated the automobile speed with high accuracy. Experiment results show that in the scenario of single automobile, SpeedTalker can achieve an average estimation error of 6.1 percent compared to radar speed guns. In the scenario of multiple automobiles, SpeedTalker can achieve an average estimation error of 9.8 percent, which is acceptable for usage. [ABSTRACT FROM AUTHOR]

With the rising of demands for novel human-computer interaction approaches in the 2D plane, a number of intelligent devices come into being. For example, Microsoft Surface Dial supports simple clicks and rotations for the interaction with computer. However, these approaches are dedicated devices, and they might require batteries or have limited functions. In this paper, we propose RF-Dial to realize a light-weight, battery-free and functional 2D human-computer interaction solution via commercial off-the-shelf (COTS) passive RFID tags. What RF-Dial shines is that it can easily turn an ordinary object, e.g., a board eraser, into an intelligent interaction device. By deploying a tag array on the side face of the object together with a dipole tag on the top face, RF-Dial cannot only track the rigid motion of the object but also detect the touch gesture of a user on the surface of the object, including translation, rotation, click, press and hold, and swipe. To do the motion tracking, RF-Dial builds a phase-based model that captures the translation and the rotation of the tagged object simultaneously, by jointly exploiting the information of phase variations and the topology of the tag array. To detect the touch gesture, RF-Dial builds an RSSI-based model that uses the impact of the touching finger on the tag antenna’s impedance to estimate the touch position in real time, which is robust to environmental factors like position or orientation. We implemented a prototype of RF-Dial with commodity RFID devices. Extensive experiments show that RF-Dial achieves an accurate rigid motion tracking, with a small error of 0.6cm for the translation tracking, and a small error of 1.9 degrees for the rotation estimation. Besides, RF-Dial can also detect the touch gesture accurately, as the 90 percent of touch position errors are less than 2.09mm. [ABSTRACT FROM AUTHOR]

Currently, the logistic industry has introduced 3D reconstruction to monitor the package placement in the warehouse. Previous 3D reconstruction solutions mainly utilize computer vision or sensor-based methods, which are restricted to the line-of-sight or the battery life. Therefore, we propose a passive RFID-based solution, called RF-3DScan, to perform 3D reconstruction on tagged packages, including the package orientation and the package stacking. The basic idea is that a moving antenna can obtain RF-signals from the tags attached on packages with the 1D linear mobile scanning. Through extracting phase differences to build angle profiles for each tag, RF-3DScan derives their relative positions, further determines the package orientation and the coarse-grained package stacking. By simply performing the 2D scanning, RF-3DScan can provide the fine-grained package stacking determination. We implement a prototype system of RF-3DScan and evaluate its performance in real settings. Our experiment results show that RF-3DScan can achieve about 92.5 percent identification accuracy of the bottom face, and an average error about 4.08 $^\circ$ ∘ of the rotation angle. For the package stacking, 1D scanning can achieve the comparable performance in comparison with 2D scanning. [ABSTRACT FROM AUTHOR]

With computer vision-based technologies, current Augmented reality (AR) systems can effectively recognize multiple objects with different visual characteristics. However, only limited degrees of distinctions can be offered among different objects with similar natural features, and inherent information about these objects cannot be effectively extracted. In this paper, we propose TaggedAR, i.e., an RFID-based approach to assist the recognition of multiple tagged objects in AR systems, by deploying additional RFID antennas to the COTS depth camera. By sufficiently exploring the correlations between the depth of field and the received RF-signal, we propose a rotate scanning-based scheme to distinguish multiple tagged objects in the stationary situation, and propose a continuous scanning-based scheme to distinguish multiple tagged human subjects in the mobile situation. By pairing the tags with the objects according to the correlations between the depth of field and RF-signals, we can accurately identify and distinguish multiple tagged objects to realize the vision of “tell me what I see” from the AR system. We have implemented a prototype system to evaluate the actual performance with case studies in a real-world environment. The experiment results show that our solution achieves an average match ratio of 91 percent in distinguishing up to dozens of tagged objects with a high deployment density. [ABSTRACT FROM AUTHOR]