MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos

Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only processing pixels with significant updates over the previous frame. However, DeltaCNN relies on static camera input. Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames. In this work, we propose MotionDeltaCNN, a sparse CNN inference framework that supports moving cameras. We introduce spherical buffers and padded convolutions to enable seamless fusion of newly unveiled regions and previously processed regions -- without increasing memory footprint. Our evaluation shows that we outperform DeltaCNN by up to 90% for moving camera videos

Road Assessment Model and Pilot Application in China

Risk assessment of roads is an effective approach for road agencies to determine safety improvement investments. It can increases the cost-effective returns in crash and injury reductions. To get a powerful Chinese risk assessment model, Research Institute of Highway (RIOH) is developing China Road Assessment Programme (ChinaRAP) model to show the traffic crashes in China in partnership with International Road Assessment Programme (iRAP). The ChinaRAP model is based upon RIOH’s achievements and iRAP models. This paper documents part of ChinaRAP’s research work, mainly including the RIOH model and its pilot application in a province in China

EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild

We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters with global body and camera trajectories for in-the-wild videos. We use body-worn, wireless electromagnetic (EM) sensors and a hand-held iPhone to record a total of 58 minutes of motion data, distributed over 81 indoor and outdoor sequences and 10 participants. Together with accurate body poses and shapes, we also provide global camera poses and body root trajectories. To construct EMDB, we propose a multi-stage optimization procedure, which first fits SMPL to the 6-DoF EM measurements and then refines the poses via image observations. To achieve high-quality results, we leverage a neural implicit avatar model to reconstruct detailed human surface geometry and appearance, which allows for improved alignment and smoothness via a dense pixel-level objective. Our evaluations, conducted with a multi-view volumetric capture system, indicate that EMDB has an expected accuracy of 2.3 cm positional and 10.6 degrees angular error, surpassing the accuracy of previous in-the-wild datasets. We evaluate existing state-of-the-art monocular RGB methods for camera-relative and global pose estimation on EMDB. EMDB is publicly available under https://ait.ethz.ch/emdbComment: Accepted to ICCV 202

Visual Estimation of Fingertip Pressure on Diverse Surfaces using Easily Captured Data

People often use their hands to make contact with the world and apply pressure. Machine perception of this important human activity could be widely applied. Prior research has shown that deep models can estimate hand pressure based on a single RGB image. Yet, evaluations have been limited to controlled settings, since performance relies on training data with high-resolution pressure measurements that are difficult to obtain. We present a novel approach that enables diverse data to be captured with only an RGB camera and a cooperative participant. Our key insight is that people can be prompted to perform actions that correspond with categorical labels describing contact pressure (contact labels), and that the resulting weakly labeled data can be used to train models that perform well under varied conditions. We demonstrate the effectiveness of our approach by training on a novel dataset with 51 participants making fingertip contact with instrumented and uninstrumented objects. Our network, ContactLabelNet, dramatically outperforms prior work, performs well under diverse conditions, and matched or exceeded the performance of human annotators

High-resolution chalcogenide fiber bundles for infrared imaging

An ordered chalcogenide fiber bundle with a high resolution for infrared imaging was fabricated using a stack-and-draw approach. The fiber bundle consisted of about 810,000 single fibers with an As2S3 glass core of 9 μm in diameter and a polyetherimide (PEI) polymer cladding of 10 μm in diameter. The As2S3 fibers showed good transparency in the 1.5–6.5 μm spectral region. It presented a resolution of ~45 lp/mm and a crosstalk of ~2.5%. Fine thermal images of a hot soldering iron tip were delivered through the fiber bundle

Construction of C-C bonds via photoreductive coupling of ketones and aldehydes in the metal-organic-framework MFM-300(Cr).

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-06-01, epub 2021-06-11Publication status: PublishedFunder: RCUK | Engineering and Physical Sciences Research Council (EPSRC); Grant(s): EP/I011870Funder: European Research Council; Grant(s): 742401Construction of C-C bonds via reductive coupling of aldehydes and ketones is hindered by the highly negative reduction potential of these carbonyl substrates, particularly ketones, and this renders the formation of ketyl radicals extremely endergonic. Here, we report the efficient activation of carbonyl compounds by the formation of specific host-guest interactions in a hydroxyl-decorated porous photocatalyst. MFM-300(Cr) exhibits a band gap of 1.75 eV and shows excellent catalytic activity and stability towards the photoreductive coupling of 30 different aldehydes and ketones to the corresponding 1,2-diols at room temperature. Synchrotron X-ray diffraction and electron paramagnetic resonance spectroscopy confirm the generation of ketyl radicals via confinement within MFM-300(Cr). This protocol removes simultaneously the need for a precious metal-based photocatalyst or for amine-based sacrificial agents for the photochemical synthesis