Title
Quantifying the uncertainty of event detection in full motion video
Author
Neumann, N.M.P.
Knegjens, R.
Den Hollander, R.
Oggero, S.
Burghouts, G.J.
Van Den Broek, S.P.
Contributor
Bouma, H. (editor)
Prabhu, R. (editor)
Stokes, R.J. (editor)
Yitzhaky, Y. (editor)
Publication year
2018
Abstract
Algorithms for the detection and tracking of objects can be combined into a system that automatically extracts relevant events from a large amount of video data. Such a system , can be particularly useful in video surveillance applications, notably to support analysts in retrieving information from hours of video while working under strict time constraints. Such data pipelines entail all sort of uncertainties, however, which can lead to erroneous detections being presented to the analyst. In this paper we present a novel method to attribute a confidence of correct detection to the output of a computer vision data pipeline. The method relies on a datadriven approach. A machine learning-based classifier is built to separate correct from erroneous detections. It is trained on features extracted from the pipeline; The features relate to both raw data properties, such as image quality, and to video content properties, such as detection characteristics. The validation of the results is done using two full motion video datasets from airborne platforms; the first being of the same type as the training set, the second being of a different type . We conclude that the result of this classifier could be used to build a confidence of correct detection, separating the True Positives from the False Positives. This confidence can furthermore be used to prioritize the detections in order of reliability. This study concludes by identifying additional work measures needed to improve the robustness of the method. © 2018 SPIE.
Subject
Detection
Full Motion Video
Machine Learning
Tracking
Uncertainty
Artificial intelligence
Error detection
Learning systems
Monitoring
Motion analysis
Pipelines
Security systems
Surface discharges
Terrorism
Video recording
Airborne platforms
Data-driven approach
Detection and tracking
Detection characteristics
Full motion video
Time constraints
Uncertainty
Video-surveillance applications
Chemical detection
To reference this document use:
http://resolver.tudelft.nl/uuid:4eb2e219-50cb-4d17-b2bb-279e2e9c45ff
TNO identifier
844195
Publisher
SPIE
ISBN
9781510621879
ISSN
0277-786X
Source
Proceedings of SPIE - The International Society for Optical Engineering, 10802
Article number
108020E
Bibliographical note
The Society of Photo-Optical Instrumentation Engineers
Document type
conference paper