Article ; Online: A railway intrusion detection method based on decomposition and semi-supervised learning for accident protection.
Accident; analysis and prevention
2023 Volume 189, Page(s) 107124
Abstract: In recent years, video surveillance has become increasingly popular in railway intrusion detection. However, it is still quite challenging to detect the intruded objects efficiently and accurately because: (a) The backgrounds of video frames generated by ...
| Abstract | In recent years, video surveillance has become increasingly popular in railway intrusion detection. However, it is still quite challenging to detect the intruded objects efficiently and accurately because: (a) The backgrounds of video frames generated by the fixed cameras are similar and only few intrusive frames are available, resulting in a lack of diversity among video frames, and further leading to over fitting of the detection models during training; (b) The intrusion of small targets or targets far from the location of camera exhibits sparsity relative to the wide monitoring view of the camera, which challenges the detection of such targets in a complex background; (c) The extreme imbalance between non-intrusive frames and intrusive frames, as well as a large number of unlabeled frames, hinder the effective training of the detection model and weaken its capacity of generalization. To tackle the above issue, this article develops an effective intrusion detection method by combining low-rank and sparse decomposition (LRSD) and Semi-supervised Support Vector Domain Description (Semi-SVDD). Firstly, LRSD is used to decompose the monitored video into a background and a foreground. Then, based on the semantic segmentation method, we extract the mask of the track region in the decomposed background, which is used to mask the foreground. Next, by using both the labeled and unlabeled frames of the masked foreground, Semi-SVDD is established for the intrusion detection. Numerical results show that the removal of background interference and the combination of the labeled and unlabeled information help to improve the performance of the proposed method, and thus superior to benchmark methods. |
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| MeSH term(s) | Humans ; Algorithms ; Accidents, Traffic/prevention & control ; Supervised Machine Learning |
| Language | English |
| Publishing date | 2023-05-27 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 210223-7 |
| ISSN | 1879-2057 ; 0001-4575 |
| ISSN (online) | 1879-2057 |
| ISSN | 0001-4575 |
| DOI | 10.1016/j.aap.2023.107124 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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