Description
Pan Tilt Traffic Cameras (PTTC) are a vital component of traffic managementsystems for monitoring/surveillance. In a real world scenario, if a vehicle is in pursuit
of another vehicle or an accident has occurred at an intersection causing traffic
stoppages, accurate and venerable data from PTTC is necessary to quickly localize
the cars on a map for adept emergency response as more and more traffic systems are
getting automated using machine learning concepts. However, the position(orientation)
of the PTTC with respect to the environment is often unknown as most of them
lack Inertial Measurement Units or Encoders. Current State Of the Art systems 1.
Demand high performance compute and use carbon footprint heavy Deep Neural
Networks(DNN), 2. Are only applicable to scenarios with appropriate lane markings or
only roundabouts, 3. Demand complex mathematical computations to determine focal
length and optical center first before determining the pose. A compute light approach
"TIPANGLE" is presented in this work. The approach uses the concept of Siamese
Neural Networks(SNN) encompassing simple mathematical functions i.e., Euclidian
Distance and Contrastive Loss to achieve the objective. The effectiveness of the
approach is reckoned with a thorough comparison study with alternative approaches
and also by executing the approach on an embedded system i.e., Raspberry Pi 3.
Details
Title
- TIPANGLE: A Machine Learning Approach for Accurate Spatial Pan and Tilt Angle Determination of Pan Tilt Traffic Cameras
Contributors
- Jagadeesha, Shreehari (Author)
- Shrivastava, Aviral (Thesis advisor)
- Gopalan, Nakul (Committee member)
- Arora, Aman (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023
Subjects
Resource Type
Collections this item is in
Note
-
Partial requirement for: M.C.St., Arizona State University, 2023
-
Field of study: Computer Science