Convolutional Neural Network for Finding Unusual Frames in Outdoor Webcam Streams
Research Mentor(s)
Wehrwein, Scott
Description
Outdoor live streaming webcams produce vast quantities of video footage that reveal rich visual information about scenes around the world and how they change. The ability to identify unique or unusual frames in these streams has potential applications in surveillance, photography, and traffic monitoring, but the volume of data makes manual analysis impractical. Existing techniques tend to focus on detecting unusual or suspicious events in a surveillance context, requiring high-level object motion analysis and rely on hand-crafted features. In this paper, we propose a deep learning-based model that uses Convolutional Neural Networks(CNNs) to solve the novel proxy task of predicting the time interval between frames, using the model’s inaccurate predictions as an indicator that something unexpected has happened in the scene. We evaluate our techniques on a new dataset of live-streaming outdoor webcam footage. Experimental results on this dataset confirm that our proposed model is capable of finding unusual frames, with 1% ~ 10% of the frames from the input videos are labeled as unusual.
Document Type
Event
Start Date
18-5-2020 12:00 AM
End Date
22-5-2020 12:00 AM
Department
Computer Science
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
Neural networks (Computer science);p Webcams
Type
Image
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Convolutional Neural Network for Finding Unusual Frames in Outdoor Webcam Streams
Outdoor live streaming webcams produce vast quantities of video footage that reveal rich visual information about scenes around the world and how they change. The ability to identify unique or unusual frames in these streams has potential applications in surveillance, photography, and traffic monitoring, but the volume of data makes manual analysis impractical. Existing techniques tend to focus on detecting unusual or suspicious events in a surveillance context, requiring high-level object motion analysis and rely on hand-crafted features. In this paper, we propose a deep learning-based model that uses Convolutional Neural Networks(CNNs) to solve the novel proxy task of predicting the time interval between frames, using the model’s inaccurate predictions as an indicator that something unexpected has happened in the scene. We evaluate our techniques on a new dataset of live-streaming outdoor webcam footage. Experimental results on this dataset confirm that our proposed model is capable of finding unusual frames, with 1% ~ 10% of the frames from the input videos are labeled as unusual.