Connecting the Complementary-view Videos: Joint Camera Identification and Subject Association

This paper presents a vehicle counting system using multi-class vehicle detection using YOLOX and multi-object tracking using ByteTrack. Counting is performed for each class of the vehicle including bus, car, motorcycle, pickup, truck, and van in a predefined region of interest (ROI). Our proposed system is designed to handle noisy and low contrast traffic videos of top and side view of the vehicles. In particular, side view videos show the occlusion of the two directional lanes which lead to vehicle occlusion problems. For object detection and classification, YOLOX shows promising mean average precision (mAP) approximately at 77.2 and 58.8 percent for top and side views, respectively, which outperforms YOLOv3 for both top and side view datasets. The counting results show that ByteTrack with YOLOX can handle vehicle occlusion which occurs in the side view videos. Counting the vehicles within an ROI can reduce fault detections for both top view and side view videos.

Pagoda[5]arene (P5), which is composed of five 2,6-dimethoxylanthracene (2,6-DMA) subunits bridged by methylene groups at 1,5 positions, was conveniently synthesized in 43% by trifluoroacetic acid ...

Nowadays, 5G profoundly impacts video surveillance and monitoring services by processing video streams at high-speed with high-reliability, high bandwidth, and secure network connectivity. It also enhances artificial intelligence, machine learning, and deep learning techniques, which require intense processing to deliver near-real-time solutions. In video surveillance, person tracking is a crucial task due to the deformable nature of the human body, various environmental components such as occlusion, illumination, and background conditions, specifically, from a top view perspective where the person’s visual appearance is significantly different from a frontal or side view. In this work, multiple people tracking framework is presented, which uses 5G infrastructure. A top view perspective is used, which offers broad coverage of the scene or field of view. To perform a person tracking deep learning-based tracking by detection framework is proposed, which includes detection by YOLOv3 and tracking by Deep SORT algorithm. Although the model is pre-trained using the frontal view images, even then, it gives good detection results. In order to further enhance the accuracy of the detection model, the transfer learning approach is adopted. In this way, a detection model takes advantage of a pre-trained model appended with an additional trained layer using top view data set. To evaluate the performance, experiments are carried out on different top view video sequences. Experimental results reveal that transfer learning improves the overall performance, detection accuracy, and reduces false positives. The deep learning detection model YOLOv3 achieves detection accuracy of 92% with a pre-trained model without transfer learning and 95% with transfer learning. The tracking algorithm Deep SORT also achieves excellent results with a tracking accuracy of 96%.

π-Pimer, π-Dimer, π-Trimer, and 1D π-Stacks in a Series of Benzene Triimide Radical Anions: Substituent-Modulated π Interactions and Physical Properties in Crystalline State

Impaired walking ability and leg health are commonly seen in broilers and can negatively impact their welfare. Commonly, walking ability and leg health are assessed manually, but this is time consuming and can be subjective. Automated approaches for scoring walking ability and leg health at the individual level could therefore have great added value. Here, we studied whether automatically extracted top-view walking features of broilers can be used as a proxy for walking ability and leg health. Top-view videos were collected of unmarked broilers walking through a corridor that was placed inside the home pen. From these videos, four top-view features were derived: 1) lateral body oscillation, calculated as deviations from the movement trajectory of the bird, 2) step count, 3) completion time, and 4) length-width ratio of the virtual bounding box encapsulating the bird while walking as an indicator of wing support. We assessed the relationship of these computer vision-based features with manual gait and leg health scores, including hock burn (HB) and footpad dermatitis (FPD). We observed that birds with worse gait scores (GS) had longer completion times, higher step counts and a trend for higher lateral body oscillation levels in the walkway setup. Unsupervised clustering using the K-means algorithm with these walking features showed potential to distinguish birds with GS3+, although differentiating between GS1 and GS2 proved more challenging. We concluded that the length-width ratio of the bounding box during walking was not a suitable proxy for poor gait. We found no relationship between top-view walking features and mild cases of HB and FPD in broilers. Overall, the results of this study indicate that top-view video recordings can provide insight into birds' walking ability, using features related to movement speed, step count and lateral body oscillation, making automated scoring more feasible on a larger scale in practice. However, these top view features provide little information about mild HB and FPD.

<div class="htmlview paragraph">A pictorial reference for the identification and location of the filaments and electrical contacts for common motor vehicle dual filament sealed beam headlamps and dual filament replaceable headlamp bulbs is presented. The filaments and contacts shown in the reference photographs are clearly identified to facilitate comparison to headlamps or bulbs under investigation.</div>

Drought events or the combination of drought and heat conditions are expected to become more frequent due to global warming, and wheat yields may fall below their long-term average. One way to increase climate-resilience of modern high-yielding varieties is by their genetic improvement with beneficial alleles from crop wild relatives. In the present study, the effect of two beneficial QTLs introgressed from wild emmer wheat and incorporated in the three wheat varieties BarNir, Zahir and Uzan was studied under well-watered conditions and under drought stress using non-destructive High-throughput Phenotyping (HTP) throughout the life cycle in a single pot-experiment. Plants were daily imaged with RGB top and side view cameras and watered automatically. Further, at two time points, the quantum yield of photosystem II was measured with a top view FluorCam. The QTL carrying near isogenic lines (NILs) were compared with their corresponding parents by t-test for all non-invasively obtained traits and for the manually determined agronomic and yield parameters. Data quality of phenotypic traits (repeatability) in the controlled HTP experiment was above 85% throughout the life cycle and at maturity. Drought stress had a strong effect on growth in all wheat genotypes causing biomass reduction from 2% up to 70% at early and late points in the drought period, respectively. At maturity, the drought caused 47–55% decreases in yield-related traits grain weight, straw weight and total biomass and reduced TKW by 10%, while water use efficiency (WUE) increased under drought by 29%. The yield-enhancing effect of the introgressed QTLs under drought conditions that were previously demonstrated under field/screenhouse conditions in Israel, could be mostly confirmed in a greenhouse pot experiment using HTP. Daily precision phenotyping enabled to decipher the mode of action of the QTLs in the different genetic backgrounds throughout the entire wheat life cycle. Daily phenotyping allowed a precise determination of the timing and size of the QTLs effect (s) and further yielded information about which image-derived traits are informative at which developmental stage of wheat during the entire life cycle. Maximum height and estimated biovolume were reached about a week after heading, so experiments that only aim at exploring these traits would not need a longer observation period. To obtain information on different onset and progress of senescence, the CVa curves represented best the ongoing senescence of plants. The QTL on 7A in the BarNir background was found to improve yield under drought by increased biomass growth, a higher photosynthetic performance, a higher WUE and a “stay green effect.”

Although evolution dynamic investigation onto electrochemically generated bubbles on electrode surfaces is crucial for releasing “bubble blocking” effects, traditional “top view” or “side view” methods make spatiotemporal interfacial revolutions difficult to achieve, which are critical for a deeper understanding and better electrode design. In this study, a novel method utilizing surface plasmon resonance (SPR) is proposed to probe bubble interfacial dynamics. By monitoring reflectivity changes and utilizing differential imaging, real‐time fluctuations in the contact area can be accurately visualized. The investigation onto bubble growth events on both aerophilic and aerophobic surfaces reveals that they occur in very distinct patterns. Due to the “necking effect”, the reflectivity of aerophilic electrodes rushed to its highest value and remained or gradually decreased, followed by a sudden drop due to bubble disengagement. While the aerophobic ones increased to the bubble release threshold after a downward parabola function, then dropped abruptly without plateau. This in situ imaging technique provides a feasible tool for bubble disengagement dynamic investigations on electrode surface. Based on current observations, it is revealed that changing the wettability of electrodes causes substantial changes in bubble evolution dynamics.

We present a fully-automatic image processing pipeline to build a set of 2D morphable models of three craniofacial profiles from orthogonal viewpoints, side view, front view and top view, using a set of 3D head surface images. Subjects in this dataset wear a close-fitting latex cap to reveal the overall skull shape. Texture-based 3D pose normalization and facial landmarking are applied to extract the profiles from 3D raw scans. Fully-automatic profile annotation, subdivision and registration methods are used to establish dense correspondence among sagittal profiles. The collection of sagittal profiles in dense correspondence are scaled and aligned using Generalised Procrustes Analysis (GPA), before applying principal component analysis to generate a morphable model. Additionally, we propose a new alternative alignment called the Ellipse Centre Nasion (ECN) method. Our model is used in a case study of craniosynostosis intervention outcome evaluation, and the evaluation reveals that the proposed model achieves state-of-the-art results. We make publicly available both the morphable models and the profile dataset used to construct it.

It is important to disclose the wake flow around cylinders for understanding the mechanisms of heat transfer enhancement in flow networks. An experimental study was performed to visualize the wake flow in the gap between two rectangular cylinders. Square and rectangular cylinders are selected at Reynolds number range of less than 600 in the experiment. The wake flows are visualized at a top view, side views and front views using the fluorescein dye illuminated by a laser light sheet. Different shapes of vortices are shown in the wake flows visualized at three views. Flow visualization allows a better understanding of vortex structures of flow in the gap between two rectangular cylinders.

A preliminary investigation of the post heat treatment for Ti6Al4V tensile specimens fabricated via selective laser melting (SLM) are reported. The resultant microstructure and mechanical behavior at room temperature, normal and parallel to the building direction, was investigated in detail. Moreover, the as-prepared samples were compared with the samples post heat treated at 600°C~800°C for 2 h. It was found that the SLM fabricated samples consisted of long columnar original β grains together with parallel acicular α' martensitic structure dominated in the β matrix, which was observed in the side view, and fully equaixed β grains pattern in the top view. The post heat treatment did not completely disrupt the layered structure, but leading to the acicular α' martensites decomposing into α platelets distributed in a metastable β matrix. Ultimate tensile strength and elongation were analyzed and explained based on the microstructure evolution. After post heat treatment, an increase in elongation was observed for the tensile samples, inducing more ductile like morphology after fracture.

BackgroundIn the last few years high-throughput analysis methods have become state-of-the-art in the life sciences. One of the latest developments is automated greenhouse systems for high-throughput plant phenotyping. Such systems allow the non-destructive screening of plants over a period of time by means of image acquisition techniques. During such screening different images of each plant are recorded and must be analysed by applying sophisticated image analysis algorithms.ResultsThis paper presents an image analysis pipeline (HTPheno) for high-throughput plant phenotyping. HTPheno is implemented as a plugin for ImageJ, an open source image processing software. It provides the possibility to analyse colour images of plants which are taken in two different views (top view and side view) during a screening. Within the analysis different phenotypical parameters for each plant such as height, width and projected shoot area of the plants are calculated for the duration of the screening. HTPheno is applied to analyse two barley cultivars.ConclusionsHTPheno, an open source image analysis pipeline, supplies a flexible and adaptable ImageJ plugin which can be used for automated image analysis in high-throughput plant phenotyping and therefore to derive new biological insights, such as determination of fitness.

<strong class="journal-contentHeaderColor">Abstract.</strong> With the advancement of computer vision, artificial intelligence, and remote sensing technologies, deep learning algorithms are increasingly used in terrestrial, airborne, and spaceborne-based fire detection systems. The performance and generalization of these data-driven fire detection algorithms, however, are restricted by the limited number and source of fire detection datasets. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. It holds rich variations in image size, resolution, illumination (day and night), scenario (indoor and outdoor), image range (far and near), viewing angle (top view and side view), platform (surveillance cameras, drones, and satellites), and data source (Internet, social media, and open-access fire datasets). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. It provides a challenging benchmark to drive the continuous evolution of fire detection models. Additionally, we formulate a unified workflow for preprocessing, annotation, and quality control of fire samples. Out-of-the-box annotations are published in four different formats for training deep learning models. Extensive performance evaluations based on classical methods show that most of the object detection models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % mAP@0.5 accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. Deep learning models trained with FASDD can be simultaneously deployed on satellites, drones, and ground sensors, thus realizing collaborative fire observation and detection in a space-air-ground integrated network environment. The dataset is available from the Science Data Bank website at <a href="https://doi.org/10.57760/sciencedb.j00104.00103" target="_blank" rel="noopener">https://doi.org/10.57760/sciencedb.j00104.00103</a> (Wang et al., 2022a).

We present photoluminescence and resonant Raman measurements of atomically thin layers of molybdenum disulfide (MoS2) in the energy range of the A and B optical transitions. We found a Stokes shift of the transition energies in the resonant Raman measurements compared to the photoluminescence. The Stokes shift increases for the bilayer compared to the single layer. Furthermore, in resonance with the A transition, the Raman intensity of the A1g mode of the bilayer is six times larger than that of the single layer. magnified imageSchematic crystal structure of molybdenum disulfide. (a) Top view (slightly tilted). (b) Side view.

An experimental setup is implemented in order to study the solidification of a drop impinging a subcooled substrate. One of the major problems relying the study of droplet solidification consist of the visualization of the solidification front within the droplet. Indeed, shadowgraphy measurement only allows the observation of the solidification front along the tri-junction liquid/solid/air. The use of the Laser Induced Fluorescence provides information on the evolution of the horizontal solidification front in a droplet over t ime. The images reconstruction of the solidification front is made by using two high-speed cameras (side view and top view). The measurements allow for the first time to observe the evolution of the solidification front geometry over t ime. The measurements, carried out on a duralumin substrate, were then compared with a 2D numerical model taking into account the heat transfers conjugated with the substrate.