Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.
The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep learning algorithms with the Boston Dynamics robotic dog Spot for the modeling of large public venues, and in its collaboration and interaction with 3D model of the large public venue, a network of surveillance cameras monitoring the area, and humans in the environment. The thesis work focuses on the exploration of the following two parts: (1) Calibrating Multiple Camera Feeds Using the Mobile Robotic Dog Spot; (2) Building 3D Live Virtual Environments in the Unity3D Game Engine.
The goal is to calibrate the network of cameras monitoring the area by using the walking Spot to generate known 3D calibration target points accurate enough and widely distributed across the field of view of a camera, and estimate the intrinsic and extrinsic parameters of the cameras. The current progress includes creating a 3D virtual environment of a robotics lab where virtual cameras take snapshots and collect locations of Spot moving through the space, which are then used to calibrate the virtual cameras.
This work seeks to address the ever-increasing security concerns in public venues and has significant scientific and societal impacts, including enhancing research in digital twins, smart surveillance, robotics, and assistive technology. Those in charge of security and protecting the public need to be supported by the latest technological advances to stay ahead of the threats. Overall, the goal of the work presented here is to make the duties of those who are tasked with protecting public places more efficient in detecting, deterring, and mitigating targeted violence.
","abstract_html":"<p id="x-x-docs-internal-guid-4316677e-7fff-7489-104b-8d6a9bd8abda">Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.</p> <p>The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep learning algorithms with the Boston Dynamics robotic dog Spot for the modeling of large public venues, and in its collaboration and interaction with 3D model of the large public venue, a network of surveillance cameras monitoring the area, and humans in the environment. The thesis work focuses on the exploration of the following two parts: (1) Calibrating Multiple Camera Feeds Using the Mobile Robotic Dog Spot; (2) Building 3D Live Virtual Environments in the Unity3D Game Engine.</p> <p>The goal is to calibrate the network of cameras monitoring the area by using the walking Spot to generate known 3D calibration target points accurate enough and widely distributed across the field of view of a camera, and estimate the intrinsic and extrinsic parameters of the cameras. The current progress includes creating a 3D virtual environment of a robotics lab where virtual cameras take snapshots and collect locations of Spot moving through the space, which are then used to calibrate the virtual cameras.</p> <p>This work seeks to address the ever-increasing security concerns in public venues and has significant scientific and societal impacts, including enhancing research in digital twins, smart surveillance, robotics, and assistive technology. Those in charge of security and protecting the public need to be supported by the latest technological advances to stay ahead of the threats. Overall, the goal of the work presented here is to make the duties of those who are tasked with protecting public places more efficient in detecting, deterring, and mitigating targeted violence.</p>","abstract_has_math":false,"creators":["Samoylov, Eltan"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhigang Zhu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T01:58:13Z","subjects":["Virtual Environments","Camera Calibration","Sensor Network","Computational Engineering","Computer Engineering","Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/1263","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhigang Zhu"]},{"key":"dc:creator","label":"Author","values":["Samoylov, Eltan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Virtual Environments","Camera Calibration","Sensor Network","Computational Engineering","Computer Engineering","Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/1263"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.
The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep learning algorithms with the Boston Dynamics robotic dog Spot for the modeling of large public venues, and in its collaboration and interaction with 3D model of the large public venue, a network of surveillance cameras monitoring the area, and humans in the environment. The thesis work focuses on the exploration of the following two parts: (1) Calibrating Multiple Camera Feeds Using the Mobile Robotic Dog Spot; (2) Building 3D Live Virtual Environments in the Unity3D Game Engine.
The goal is to calibrate the network of cameras monitoring the area by using the walking Spot to generate known 3D calibration target points accurate enough and widely distributed across the field of view of a camera, and estimate the intrinsic and extrinsic parameters of the cameras. The current progress includes creating a 3D virtual environment of a robotics lab where virtual cameras take snapshots and collect locations of Spot moving through the space, which are then used to calibrate the virtual cameras.
This work seeks to address the ever-increasing security concerns in public venues and has significant scientific and societal impacts, including enhancing research in digital twins, smart surveillance, robotics, and assistive technology. Those in charge of security and protecting the public need to be supported by the latest technological advances to stay ahead of the threats. Overall, the goal of the work presented here is to make the duties of those who are tasked with protecting public places more efficient in detecting, deterring, and mitigating targeted violence.
"]},{"key":"dc:title","label":"Title","values":["Virtual Environment Creation and Camera Calibration for Soft Target Identification and Assistance in Crowded Spaces with a Sensor Network and a Robotic Dog"]}]}],"canonical_facts":{"dc:contributor":["Zhigang Zhu"],"dc:creator":["Samoylov, Eltan"],"dc:date.available":["2025-05-25T07:00:00Z"],"dc:description.abstract":["Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.
The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep learning algorithms with the Boston Dynamics robotic dog Spot for the modeling of large public venues, and in its collaboration and interaction with 3D model of the large public venue, a network of surveillance cameras monitoring the area, and humans in the environment. The thesis work focuses on the exploration of the following two parts: (1) Calibrating Multiple Camera Feeds Using the Mobile Robotic Dog Spot; (2) Building 3D Live Virtual Environments in the Unity3D Game Engine.
The goal is to calibrate the network of cameras monitoring the area by using the walking Spot to generate known 3D calibration target points accurate enough and widely distributed across the field of view of a camera, and estimate the intrinsic and extrinsic parameters of the cameras. The current progress includes creating a 3D virtual environment of a robotics lab where virtual cameras take snapshots and collect locations of Spot moving through the space, which are then used to calibrate the virtual cameras.
This work seeks to address the ever-increasing security concerns in public venues and has significant scientific and societal impacts, including enhancing research in digital twins, smart surveillance, robotics, and assistive technology. Those in charge of security and protecting the public need to be supported by the latest technological advances to stay ahead of the threats. Overall, the goal of the work presented here is to make the duties of those who are tasked with protecting public places more efficient in detecting, deterring, and mitigating targeted violence.
"],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/1263"],"dc:subject":["Virtual Environments","Camera Calibration","Sensor Network","Computational Engineering","Computer Engineering","Robotics"],"dc:title":["Virtual Environment Creation and Camera Calibration for Soft Target Identification and Assistance in Crowded Spaces with a Sensor Network and a Robotic Dog"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:58:13Z"}