5th Years Master in Computer Science Thesis Presentation
— 11:00am
Location:
In Person and Virtual - ET
-
Wean Hall 5409 and Livestreamd
Speaker:
TIFFANY MA
,
Masters StudentComputer Science DepartmentCarnegie Mellon University
https://www.linkedin.com/in/tiffany-ma-2021
Mining Spatial-Temporal Attributes of Anomalies through Large Ego-Vehicle Dataset
In recent years, an increasing amount of urban visual big data has been collected through a diverse range of sources, such as taxi vehicle records, video from surveillance cameras, or images captured by mobile devices. The large collection of urban data contains rich implicit information that can help in numerous downstream tasks, such as monitoring for construction management companies, planning for government units, etc. However, it is challenging to efficiently extract the desired information from a dataset of such a large scale. In this work, we focus on developing methods for extracting the spatial attribute and the temporal attribute from these urban visual data. Specifically, we introduce a method of organizing large-scale urban visual data into a spatial temporal data structure by mining attributes inherent in the data. We demonstrate the effectiveness of our method by using videos captured by the exterior camera of buses to detect and analyze work zones within the captured videos. The raw set of bus data needs to be further preprocessed into a spatial-temporal data structure. Next, we exploit the rich spatial and temporal attributes of bus data in the application of work zone detection and analysis. The goal of this work is to demonstrate the effectiveness of using spatial and temporal attributes to break down large-scale urban visual data and extract insights from large-scale unlabeled data. Thesis Committee: Srinivasa Narasimhan (Chair) Christoph Mertz Stephen Smith Additional Information
In Person and Zoom Participation. See announcement.
For More Information:
tracyf@cs.cmu.edu