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Temporal segmentation of traffic videos based on traffic phase discovery

Ahmadi, P ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1109/NOMS.2016.7502987
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2016
  4. Abstract:
  5. 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
  6. Keywords:
  7. Fully sparse topic model (FSTM) ; Topic model ; Traffic phase detection ; Traffic signals ; Video cameras ; As distribution ; Phase detection ; Prior knowledge ; Temporal segmentations ; Temporal video segmentation ; Topic modeling ; Traffic phase ; Video sequences ; Highway traffic control
  8. Source: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, 25 April 2016 through 29 April 2016 ; 2016 , Pages 1197-1202 ; 9781509002238 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7502987/?reload=true