A computer vision system for detecting and analysing critical events in cities

Whether for commuting or leisure, cycling is a growing transport mode in many cities worldwide. However, it is still perceived as a dangerous activity. Although serious incidents related to cycling leading to major injuries are rare, the fear of getting hit or falling hinders the expansion of cycling as a major transport mode. Indeed, it has been shown that focusing on serious injuries only touches the tip of the iceberg. Near miss data can provide much more information about potential problems and how to avoid risky situations that may lead to serious incidents. Unfortunately, there is a gap in the knowledge in identifying and analysing near misses. This hinders drawing statistically significant conclusions to provide measures for the built-environment that ensure a safer environment for people on bikes. In this research, we develop a method to detect and analyse near misses and their risk factors using artificial intelligence. This is accomplished by analysing video streams linked to near miss incidents within a novel framework relying on deep learning and computer vision. This framework automatically detects near misses and extracts their risk factors from video streams before analysing their statistical significance. It also provides practical solutions implemented in a camera with embedded AI (URBAN-i Box) and a cloud-based service (URBAN-i Cloud) to tackle the stated issue in the real-world settings for use by researchers, policy-makers, or citizens. The research aims to provide human-centred evidence that may enable policy-makers and planners to provide a safer built environment for cycling in London, or elsewhere. More broadly, this research aims to contribute to the scientific literature with the theoretical and empirical foundations of a computer vision system that can be utilised for detecting and analysing other critical events in a complex environment. Such a system can be applied to a wide range of events, such as traffic incidents, crime or overcrowding

FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment.Comment: Set to be published in the PLoS ONE Journa

ImageSig: A signature transform for ultra-lightweight image recognition

This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. It is striking to the authors that it achieves: a) an accuracy for 64 X 64 RGB images that exceeds many of the state-of-the-art methods and simultaneously b) requires orders of magnitude less FLOPS, power and memory footprint. The pretrained model can be as small as 44.2 KB in size. ImageSig shows unprecedented performance on hardware such as Raspberry Pi and Jetson-nano. ImageSig treats images as streams with multiple channels. These streams are parameterized by spatial directions. We contribute to the functionality of signature and rough path theory to stream-like data and vision tasks on static images beyond temporal streams. With very few parameters and small size models, the key advantage is that one could have many of these "detectors" assembled on the same chip; moreover, the feature acquisition can be performed once and shared between different models of different tasks - further accelerating the process. This contributes to energy efficiency and the advancements of embedded AI at the edge

Di-n-butyl­bis­(N-ethyl-N-phenyl­dithio­carbamato-κS)tin(IV)

The title compound, [Sn(C4H9)2(C9H10NS2)2], features a tetra­hedrally coordinated SnIV atom; the dithio­carbamate ligands coordinate in a monodentate fashion, accompanied by two n-butyl chains. The non-coordinating thione S atoms are each proximate to the SnIV atom [3.0136 (7) and 2.9865 (8) Å], giving rise to distortions from the ideal geometry as evident in the wide C—Sn—C bond angle of 139.06 (12) °. In the crystal, C—H⋯S inter­actions lead to the formation of a linear supra­molecular chain along the b axis. The chains are aligned into layers by C—H⋯π inter­actions, and the layers stack along [001]. One of the ethyl groups is statistically disordered over two sets of sites

WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice. What has been achieved to-date is rather sectorial models that address limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep Convolutional Neural Network (CNN) models, so-called the WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as Dawn/dusk, day and night for time detection, and glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. The WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used not limited to: autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.Comment: 11 pages, 8 figure

A qualitative study exploring public perceptions on the role of community pharmacists in Dubai

Background: The role of community pharmacists is very important due to their access to primary care patients and expertise. For this reason, the interaction level between pharmacists and patients should be optimized to ensure enhanced delivery of pharmacy services. Objective: To gauge perceptions and expectations of the public on the role of community pharmacists in Dubai, United Arab Emirates (UAE). Methods: Twenty five individuals were invited to participate in 4 separate focus group discussions. Individuals came from different racial groups and socioeconomic backgrounds. Interviews were audio-recorded and transcribed. Using thematic analysis, two reviewers coded all transcripts to identify emerging themes. Appropriate measures were taken to ensure study rigor and validity. Results: All facilitators and barriers that were identified were grouped into 5 distinct themes. The pharmacist as a healthcare professional in the public mind was the most prominent theme that was discussed in all 4 focus groups. Other themes identified were, in decreasing order of prevalence, psychological perceptions towards pharmacists, important determinants of a pharmacist, the pharmacy as a unique healthcare provider, and control over pharmacies by health authorities

The role of pharmacists in developing countries: The current scenario in the United Arab Emirates

AbstractPharmacy practice has passed several rounds of advancements over the past few years. It had changed the traditional positioning criteria of pharmacists as business people into patient-centered healthcare professionals. This worldwide shift is increasingly accumulating pressure on UAE pharmacists to turn up into better level of service providing accompanied with higher demand of inter-personal skills and intellectual capabilities. This can be accomplished through stressing the significance of continuing pharmacy education in basic sciences as well as social and administrative pharmacy techniques and its collaboration in elevating the quality of pharmacy practice in the UAE

predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.Comment: 26 page