{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/389254"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/389254","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"A 4D photogrammetric system for studying flow-like landslides","abstract":"This thesis presents a photogrammetric system developed for experimental studies of laboratory landslide models. The hardware captures granular flows as time-series three-dimensional (4D) point clouds, producing high-quality digital datasets of laboratory landslide experiments. The software incorporates a robust tracking algorithm, enabling the calculation of flow velocity vectors at the single-particle or grain scale with high spatial and temporal resolution, even amid the fast and tumbling flows. We begin by describing the development of the photogrammetric hardware, which forms the foundation of this research. The core component is a stereo camera system consisting of two setups: a high-speed system optimized for motion tracking and a standard-speed system optimized for static scene scanning. Additionally, we discuss the peripheral hardware essential for high-speed imaging and laboratory landslide experiments. Next, we address the control software, which ensures synchronization across multiple cameras—a crucial aspect of a stereo vision system. We also outline the recommended system parameters, providing guidelines for configuring high-speed camera systems in a laboratory environment. The core software breakthrough lies in the implementation of a tracking algorithm for single-particle motion in granular flows. This algorithm utilizes several computer vision techniques to preprocess raw image data. Building on this foundation, we outline the steps of the original Shade-SIFT Matching algorithm and the Chain Tracking algorithm, which achieves particle tracking in two-dimensional images and three-dimensional space, respectively. The target application of this research is measuring movement distribution in landslide experiments. In the concluding stage of the study, the system’s capability for surface topology analysis and velocity mapping is demonstrated through granular column collapse flume experiments.","abstract_html":"This thesis presents a photogrammetric system developed for experimental studies of laboratory landslide models. The hardware captures granular flows as time-series three-dimensional (4D) point clouds, producing high-quality digital datasets of laboratory landslide experiments. The software incorporates a robust tracking algorithm, enabling the calculation of flow velocity vectors at the single-particle or grain scale with high spatial and temporal resolution, even amid the fast and tumbling flows. We begin by describing the development of the photogrammetric hardware, which forms the foundation of this research. The core component is a stereo camera system consisting of two setups: a high-speed system optimized for motion tracking and a standard-speed system optimized for static scene scanning. Additionally, we discuss the peripheral hardware essential for high-speed imaging and laboratory landslide experiments. Next, we address the control software, which ensures synchronization across multiple cameras—a crucial aspect of a stereo vision system. We also outline the recommended system parameters, providing guidelines for configuring high-speed camera systems in a laboratory environment. The core software breakthrough lies in the implementation of a tracking algorithm for single-particle motion in granular flows. This algorithm utilizes several computer vision techniques to preprocess raw image data. Building on this foundation, we outline the steps of the original Shade-SIFT Matching algorithm and the Chain Tracking algorithm, which achieves particle tracking in two-dimensional images and three-dimensional space, respectively. The target application of this research is measuring movement distribution in landslide experiments. In the concluding stage of the study, the system’s capability for surface topology analysis and velocity mapping is demonstrated through granular column collapse flume experiments.","abstract_has_math":false,"creators":["He, Zhiwei"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Liang, Dongfang"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-03","date_published":"2025-04-03","updated_at":"2026-07-24T01:33:05Z","subjects":["4D","Photogrammetry","Flow-like Landslide"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/5cbba5ec-f289-4b35-a78a-02213c72c843/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.121232","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Liang, Dongfang"]},{"key":"dc:creator","label":"Author","values":["He, Zhiwei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-04-03"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/389254"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["4D","Photogrammetry","Flow-like Landslide"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/5cbba5ec-f289-4b35-a78a-02213c72c843/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-09-10"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.121232"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/a14c33fb-83d3-4d75-b682-7d0a9cfb3405/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents a photogrammetric system developed for experimental studies of laboratory landslide models. 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We also outline the recommended system parameters, providing guidelines for configuring high-speed camera systems in a laboratory environment. The core software breakthrough lies in the implementation of a tracking algorithm for single-particle motion in granular flows. This algorithm utilizes several computer vision techniques to preprocess raw image data. Building on this foundation, we outline the steps of the original Shade-SIFT Matching algorithm and the Chain Tracking algorithm, which achieves particle tracking in two-dimensional images and three-dimensional space, respectively. The target application of this research is measuring movement distribution in landslide experiments. 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