Infrastructure Engineering - Research Publications

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    Pose-aware monocular localization of occluded pedestrians in 3D scene space
    Rahimi, MM ; Khoshelham, K ; Stevenson, M ; Winter, S (Elsevier BV, 2021-12-01)
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    Decentralized management of ephemeral traffic incidents
    Hu, W ; Chen, B ; Winter, S ; Khoshelham, K (WILEY, 2022-08)
    Abstract Ephemeral traffic incidents, such as a fallen tree on a road, pose traffic safety hazards, and impact locally on traffic. While these incidents are neither predictable nor persistent, their existence is relevant for all vehicles planning to pass by while the impact continues. This article develops a novel communication strategy for vehicular ad hoc networks aiming to inform all the affected vehicles, while involving only the minimum number of non‐affected vehicles. This strategy exploits time geography as a spatial and temporal filter, ensuring also that the information broadcasting timely terminates when the incident is over. Agent‐based traffic simulations show that, when a road is temporarily blocked due to an ephemeral incident, the proposed decentralized information management model achieves significant improvement in travel efficiency and automatically updates outdated incident information in time.
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    A Multi-Camera Tracker for Monitoring Pedestrians in Enclosed Environments
    Wu, X ; Winter, S ; Khoshelham, K ; Alamaniotis, M ; Pan, S (IEEE, 2020)
    Multi-camera pedestrians tracking is a challenging computer vision task. We propose a multi-camera tracker for monitoring pedestrians in an enclosed shopping environment. We assess the performance of the multi-camera tracker in a case study, tracking customers in a food and speciality market hall. Our multi-camera tracker tracks customers' walking between the stalls in the market. The information is useful for market management, visitor safety, and other potential application areas.
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    A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences
    Acharya, D ; Singha Roy, S ; Khoshelham, K ; Winter, S (MDPI, 2020-10)
    Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information from synthetic image sequences to improve localisation accuracy. A deep Bayesian recurrent CNN is fine-tuned using synthetic image sequences obtained from a building information model (BIM) to regress the pose of real image sequences. The results of the experiments indicate that the proposed approach estimates a smoother trajectory with smaller inter-frame error as compared to existing methods. The achievable accuracy with the proposed approach is 1.6 m, which is an improvement of approximately thirty per cent compared to the existing approaches. A Keras implementation can be found in our Github repository.