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Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing...
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Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera. However, capturing the motion and global position only from a sparse set of inertial sensors is inherently ambiguous and challenging. In consequence, recent state-of-the-art methods can barely handle very long period motions, and unrealistic artifacts are common due to the unawareness of physical constraints. To this end, we present the first method which combines a neural kinematics estimator and a physics-aware motion optimizer to track body motions with only 6 inertial sensors. The kinematics module first regresses the motion status as a reference, and then the physics module refines the motion to satisfy the physical constraints. Experiments demonstrate a clear improvement over the state of the art in terms of capture accuracy, temporal stability, and physical correctness.
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the...
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the...
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RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.
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Accurate and real-time instance segmentation on mobile devices enables a wide spectrum of applications such as augmented reality, context-aware inspection and environ-mental cognition. However, the computation resource demanded by...
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Accurate and real-time instance segmentation on mobile devices enables a wide spectrum of applications such as augmented reality, context-aware inspection and environ-mental cognition. However, the computation resource demanded by instance segmentation impedes its deployment on resource-constrained commercial mobile devices. Prior studies enable smartphones to conduct computational-intensive tasks in real-time with the assistance of an edge server. However, simply applying an edge-assisted framework hardly achieves delightful segmentation performance due to the movements of devices and targets, pixel-level precision requirements, and huge computational overhead even for edge nodes. This work proposes edgeIS, an edge-assisted system that enables real-time and accurate instance segmentation on mobile devices. edgeIS embraces the mobile device sensing ability of surroundings and its own motion, and redesigns an innovative mobile-edge collaboration paradigm suitable for segmentation tasks. We implement edgeIS on a lightweight edge node and different mobile devices. Extensive experiments are conducted under four datasets. The results show that edgeIS can run on mobile devices in real-time and achieve a 0.92 segmentation IoU, outperforming existing state-of-the-art solutions. We further embed edgeIS in an AR-based inspection system deployed in an oil field and the performance of edgeIS meets the demand of the industrial scenario.
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Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only sch...
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Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only schedule the deterministic transmission of time-triggered critical traffic (TCT), neglecting the other type of traffic in industrial cyber physical systems, i.e., event-triggered critical traffic (ECT). So in this work, we propose a new paradigm for TSN scheduling named E-TSN, which can provide deterministic transmission for both TCT and ECT. The three techniques of E-TSN, i.e., probabilistic stream, prioritized slot sharing, and prudent reservation, enable the deterministic transmission of ECT in TSN, and at the same time, protect TCT from the impacts of ECT. We also develop and make public a TSN evaluation toolkit to fill the gap in TSN study between algorithm design and experimental validation. The experiments show that E-TSN can reduce the latency and jitter of ECT by at least an order of magnitude compared to state-of-the-art methods. By enabling reliable and timely delivery of ECT in TSN for the first time, E-TSN can broaden the application scope of TSN in industry.
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In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical i...
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In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical in handling these problems. However, completely removing the eyeglasses is challenging because the lighting effects (e.g., cast shadows) caused by them are often complex. In this paper, we propose a novel framework to remove eyeglasses as well as their cast shadows from face images. The method works in a detect-then-remove manner, in which eyeglasses and cast shadows are both detected and then removed from images. Due to the lack of paired data for supervised training, we present a new synthetic portrait dataset with both intermediate and final supervisions for both the detection and removal tasks. Furthermore, we apply a cross-domain technique to fill the gap between the synthetic and real data. To the best of our knowledge, the proposed technique is the first to remove eyeglasses and their cast shadows simultaneously. The code and synthetic dataset are available at https://gethub.com/StoryMY/take-off-eyeglasses.
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Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for im...
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Backscatter communication, due to its low energy consumption, attract a broad range of applications. The throughput of such low-power communication is however limited. Parallel backscatter is deemed as a promising technique for improving the overall throughput by enabling concurrent transmissions of the backscattering tags. The state-of-the-art approaches for parallel backscatter assume that all the states of the collided signals are distinguishable in the In-phase and Quadrature (IQ) signal plane. In this paper, we disclose the superclustering phenomenon that makes the assumption untenable and significantly degrades the overall performance. Moreover, we observe that the indistinguishable states at different channels are not the same due to the intrinsic channel diversity. Motivated by the observation, we propose Canon, an approach that exploits the channel diversity of the backscatter tags for reliable parallel decoding. In Canon, we address two critical challenges: (i) designing the Multi-Carrier Backscatter (MCB) module to extract the collided signals simultaneously from multiple channels, (ii) designing the Multi-Channel Cluster Union (MCCU) algorithm to distinguish each state of the collided signals. The experiments demonstrate that Canon can achieve over 10x higher throughput than the state-of-the-art approaches.
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Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious...
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Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into Time-Sensitive Networking (TSN) switches. By combining control and transmission functions in switches, CaaS virtualizes the industrial TSN network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.
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Time-Sensitive Networking (TSN) has been considered the most promising network paradigm for time-critical applications (e.g., industrial control) and traffic scheduling is the core of TSN to ensure low latency and determinism. Wit...
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Time-Sensitive Networking (TSN) has been considered the most promising network paradigm for time-critical applications (e.g., industrial control) and traffic scheduling is the core of TSN to ensure low latency and determinism. With the demand for flexible production increases, industrial network topologies and settings change frequently due to pipeline switches. As a result, there is a pressing need for a more efficient TSN scheduling algorithm. In this paper, we propose DeepScheduler, a fast and scalable flow-aware TSN scheduler based on deep reinforcement learning. In contrast to prior work that heavily relies on expert knowledge or problem-specific assumptions, DeepScheduler automatically learns effective scheduling policies from the complex dependency among data flows. We design a scalable neural network architecture that can process arbitrary network topologies with informative representations of the problem, and decompose the problem decision space for efficient model training. In addition, we develop a suite of TSN-compatible testbeds with hardware-software co-design and DeepScheduler integration. Extensive experiments on both simulation and physical testbeds show that DeepScheduler runs >150/5 times faster and improves the schedulability by 36%/39% compared to state-of-the-art heuristic/expert-based methods. With both efficiency and effectiveness, DeepScheduler makes scheduling no longer an obstacle towards flexible manufacturing.
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RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlu...
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RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlusions. Based on these observations, we propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network. Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrust-worthy motions and enables robust tracking. Experimental results on public datasets and our own recorded data show that our technique outperforms existing single-view-based real-time methods by a large margin. With the reduction of the motion errors, the proposed technique can handle long and challenging motion sequences. Please check out the project page for sequence results: https://wenbinlin.github.io/OcclusionFusion.
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