Multiple Frame Point Cloud Object Detection Using Feature Fusion

Huang, Minyuan

Multiple Frame Point Cloud Object Detection Using Feature Fusion

dc.contributor.advisor	Leung, Henry
dc.contributor.author	Huang, Minyuan
dc.contributor.committeemember	Yang, Hongzhou
dc.contributor.committeemember	Carriere, Jay
dc.date.accessioned	2023-09-28T21:15:47Z
dc.date.available	2023-09-28T21:15:47Z
dc.date.issued	2023-09-21
dc.description.abstract	Object detection has important applications in the field of autonomous driving. While 2D object detection lacks depth information and suffers from the scale problems of objects, 3D point cloud object detection is a more suitable alternative for autonomous driving. However, due to the character of point cloud data, sparsity is an inevitable problem in point cloud object detection, and single-frame methods still have their limitations. This research investigates using multiple frame point cloud data to enhance detection precision. Facing the sparsity challenge in point cloud data, single-frame detection methods perform detection independently without all the data together to improve detection. However, with proper fusion scheme, using multiple frame data can be similar to using denser lidar. In this work, multiple association and fusion methods are discussed. And finally a probability-based proposal feature fusion (PPFF) module is proposed. Chapter 3 proposes a two-stage point cloud object detection method using proposal feature fusion. Proposal association methods and feature fusion methods are discussed. In Chapter 4, the MHT tracker is discussed, and in Chapter 5, combined with the knowledge of MHT, a probability-based proposal feature fusion (PPFF) module for multiple-frame point cloud object detection is proposed. We compared our methods to current multiple-frame methods to show the improvement and effectiveness of our methods.
dc.identifier.citation	Huang, M. (2023). Multiple frame point cloud object detection using feature fusion (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117177
dc.identifier.uri	https://doi.org/10.11575/PRISM/42019
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	autonomous driving
dc.subject	3D object detection
dc.subject	multiple frame point clouds
dc.subject	feature and data fusion
dc.subject.classification	Engineering--Electronics and Electrical
dc.title	Multiple Frame Point Cloud Object Detection Using Feature Fusion
dc.type	master thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.