Infrastructure Engineering - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/347

Filters

2020 3
Reset filters

Settings
Statistics
Citations
Type: Conference Paper × Start date: 2020 × End date: 2021 × Author: Khoshelham, Kourosh ×

Search Results

Now showing 1 - 3 of 3
  • Item
    Thumbnail Image
    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.
  • Item
    Thumbnail Image
    The ISPRS Benchmark on Indoor Modelling: Preliminary Results
    Khoshelham, K ; Tran, H ; Acharya, D ; Díaz Vilariño, L ; Kang, Z ; Dalyot, S (Copernicus GmBH, 2020-08-06)
    Automated 3D reconstruction of indoor environments from point clouds has been a topic of intensive research in recent years. Different methods developed for the generation of 3D indoor models have achieved promising results on different case studies. However, a comprehensive evaluation and comparison of the performance of these methods has not been available. This paper presents the preliminary results of the ISPRS benchmark on indoor modelling, an initiative of Working Group IV/5 to benchmark the performance of indoor modelling methods using a public dataset and a comprehensive evaluation framework. The performances of the different methods are compared through geometric quality evaluation of the reconstructed models in terms of completeness, correctness, and accuracy of wall elements. The results show that the reconstruction methods generally achieve high completeness but lower correctness for the reconstructed models while accuracies range from 0.5 cm to 6.7 cm.
  • Item
    Thumbnail Image
    Indoor LiDAR relocalization based on deep learning using a 3D model
    Zhao, H ; Acharya, D ; Tomko, M ; Khoshelham, K (Copernicus GmBH, 2020-08-06)
    Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for LiDAR sensors in indoor environments to estimate the initial LiDAR pose using a CNN pose regression network trained using a 3D model. A set of synthetic LiDAR frames are generated from the 3D model with known poses. Each LiDAR range image is a one-channel range image, used to train the CNN pose regression network from scratch to predict the initial sensor location and orientation. The CNN regression network trained by synthetic range images is used to estimate the poses of the LiDAR using real range images captured in the indoor environment. The results show that the proposed CNN regression network can learn from synthetic LiDAR data and estimate the pose of real LiDAR data with an accuracy of 1.9 m and 8.7 degrees.