Temporal segmentation of traffic videos based on traffic phase discovery

In this paper, the topic model is adopted to learn traffic phases from video sequence. Phase detection is applied to determine where a video clip is in the traffic light sequence. Each video clip is labeled by a certain traffic phase, based on which, videos are segmented clip by clip. Using topic mo...

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Bibliographic Details
Main Authors: Ahmadi, Parvin, Kaviani, Razie, Gholampour, Iman, Tabandeh, Mahmoud
Format: Conference Proceeding
Language:English
Subjects:
Clips
Computer vision
Context
Feature extraction
Fully Sparse Topic Model (FSTM)
Image motion analysis
Mathematical analysis
Mathematical models
Perception
temporal video segmentation
topic model
Traffic engineering
Traffic flow
Traffic phase detection
Traffic signals
Training
Vectors (mathematics)
Videos
Visualization
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Description
Summary:In this paper, the topic model is adopted to learn traffic phases from video sequence. Phase detection is applied to determine where a video clip is in the traffic light sequence. Each video clip is labeled by a certain traffic phase, based on which, videos are segmented clip by clip. Using topic models, without any prior knowledge of the traffic rules, activities are detected as distributions over quantized optical flow vectors. Then, traffic phases are discovered as clusters over activities according to the traffic signals. We employ the Fully Sparse Topic Model (FSTM) as the topic model. The results show that our method can successfully discover both activities and traffic phases which make veracious description and perception of traffic scenes.
ISSN:2374-9709
DOI:10.1109/NOMS.2016.7502987