Comparison of Two Methods for Stationary Incident Detection Based on Background Image
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909651415597056 |
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| author | Ghimire, Deepak Lee, Joonwhoan |
| author_facet | Ghimire, Deepak Lee, Joonwhoan |
| contents | In general, background subtraction-based methods are used to detect moving objects in visual tracking applications. In this paper, we employed a background subtraction-based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection, and we compare those in terms of detection performance and computational complexity. In the first approach, we used a single background, and in the second approach, we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped objects. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short-time fully occlusion, and illumination changes, and it can operate in real time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparison of Two Methods for Stationary Incident Detection Based on Background Image Ghimire, Deepak Lee, Joonwhoan Computer Vision and Pattern Recognition In general, background subtraction-based methods are used to detect moving objects in visual tracking applications. In this paper, we employed a background subtraction-based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection, and we compare those in terms of detection performance and computational complexity. In the first approach, we used a single background, and in the second approach, we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped objects. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short-time fully occlusion, and illumination changes, and it can operate in real time. |
| title | Comparison of Two Methods for Stationary Incident Detection Based on Background Image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.14256 |