Browsing by Author "Singh, Siddharth"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    A review of state-of-the-art 6D pose estimation and its applications in space operations
    (Council of European Aerospace Societies (CEAS) , 2024-06-11) Singh, Siddharth; Shin, Hyo-sang; Tsourdos, Antonios; Felicetti, Leonard
    Increase in autonomous systems now requires for these systems to work in close proximity of other objects in their environments, with many tasks that need to be done on environment objects for eg., assembly, transportation, rendezvous, docking, or to avoid them like collision detections/avoidance, path planning etc. In this literature review we discuss machine learning based algorithms that solve the first step of vision-based autonomous systems i.e., vision based pose estimation. This paper presents a critical review in advancements of 6D pose estimation using both 2D and 3D input data, and compare how they deal with the challenges shared by the computer-vision based localisation problem. We also look over algorithms with their applications in space based tasks like in-orbit docking, rendezvous and the challenges that come with space-vision applications. To conclude the review we also highlight niche problems and possible avenues for future research.
  • Thumbnail Image
    ItemOpen Access
    Vision-based fall detection in aircraft maintenance environment with pose estimation
    (IEEE, 2022-10-13) Osigbesan, Adeyemi; Barrat, Solene; Singh, Harkeerat; Xia, Dongzi; Singh, Siddharth; Xing, Yang; Guo, Weisi; Tsourdos, Antonios
    Fall-related injuries at the workplace account for a fair percentage of the global accident at work claims according to Health and Safety Executive (HSE). With a significant percentage of these being fatal, industrial and maintenance workshops have great potential for injuries that can be associated with slips, trips, and other types of falls, owing to their characteristic fast-paced workspaces. Typically, the short turnaround time expected for aircraft undergoing maintenance increases the risk of workers falling, and thus makes a good case for the study of more contemporary methods for the detection of work-related falls in the aircraft maintenance environment. Advanced development in human pose estimation using computer vision technology has made it possible to automate real-time detection and classification of human actions by analyzing body part motion and position relative to time. This paper attempts to combine the analysis of body silhouette bounding box with body joint position estimation to detect and categorize in real-time, human motion captured in continuous video feeds into a fall or a non-fall event. We proposed a standard wide-angle camera, installed at a diagonal ceiling position in an aircraft hangar for our visual data input, and a three-dimensional convolutional neural network with Long Short-Term Memory (LSTM) layers using a technique we referred to as Region Key point (Reg-Key) repartitioning for visual pose estimation and fall detection.