Print Email Facebook Twitter Congestion Detection Through Velocity Estimation Using a Monocular Camera Title Congestion Detection Through Velocity Estimation Using a Monocular Camera Author Baltus, Jelle (TU Delft Mechanical Engineering) Contributor Grammatico, S. (mentor) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2024-03-28 Abstract This thesis report aims to answer the following research question: “Is it possible to estimate relative velocities of vehicles surrounding the ego vehicle using a monocular camera with such an accuracy that meaningful conclusions can be made about the current traffic state?” To answer this question, a velocity estimation algorithm is developed in three major parts: object detection, object tracking with detections and velocity estimation using tracked 2D objects. For the detection part, a version of the YOLOv3 (You Only Look Once version 3) single shot detection neural network is used. For object tracking with detections, the Simple Online and Realtime Tracking (SORT) algorithm is used. The last part, velocity estimation using tracked 2D objects, a state-of-the-art method using a neural network is compared to a novel proposed method, using a 2D to 3D map in combination with a kalman filter using a constant velocity model. The results of the detection and tracking parts were good enough to reason that they are used as a base of the velocity estimation algorithm. When comparing the-state-of-the-art velocity estimation algorithm and the novel approach, the errors of the novel approach were significantly higher, and the results of the state-of-the-art methods could not be replicated. This means that the research question of this thesis can be answered with yes, it is possible to estimate relative velocities of surrounding vehicles, however the resulting estimation errors are too high to make meaningful conclusions about the current traffic state. To reference this document use: http://resolver.tudelft.nl/uuid:2c11c923-56dc-4b50-8544-fe899ef3f933 Part of collection Student theses Document type master thesis Rights © 2024 Jelle Baltus Files PDF Thesis_Report_Jelle_Baltus.pdf 4.36 MB Close viewer /islandora/object/uuid:2c11c923-56dc-4b50-8544-fe899ef3f933/datastream/OBJ/view