Semantic Topic Analysis of Traffic Camera Images
Name
Semantic_Topic_Analysis_of_Traffic_Camera_Images.pdf
Description
Submitted version
Size
2.54 MB
Format
Adobe PDF
Checksum (MD5)
62574a2dc05fa6a6d1ec6793f17cb501
Author(s)
Liu, Jeffrey
Weinert, Andrew J.
Amin, Saurabh
Date Issued
November 2018
Journal
Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems
Publisher
IEEE
Citation
Liu, Jeffrey, Andrew Weinert, and Saurabh Amin. "Semantic Topic Analysis of Traffic Camera Issues." 1st International Conference on Intelligent Transportation Systems, November 2018, Maui, HI, USA, IEEE, 2018.
Version
Original manuscript
Abstract
Traffic cameras are commonly deployed monitoring components in road infrastructure networks, providing operators visual information about conditions at critical points in the network. However, human observers are often limited in their ability to process simultaneous information sources. Recent advancements in computer vision, driven by deep learning methods, have enabled general object recognition, unlocking opportunities for camera-based sensing beyond the existing human observer paradigm. In this paper, we present a Natural Language Processing-inspired approach, entitled Bag-of-Label-Words (BoLW), for analyzing image data sets using exclusively textual labels. The BoLW model represents the data in a conventional matrix form, enabling data compression and decomposition techniques, while preserving semantic interpretability. We apply the Latent Dirichlet Allocation topic model to decompose the label data into a small number of semantic topics. To illustrate our approach, we use freeway camera images collected from the Boston area between December 2017-January 2018. We analyze the cameras' sensitivity to weather events; identify temporal traffic patterns; and analyze the impact of infrequent events, such as the winter holidays and the 'bomb cyclone' winter storm. This study demonstrates the flexibility of our approach, which allows us to analyze weather events and freeway traffic using only traffic camera image labels.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Lincoln Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/itsc.2018.8569449
