Fallen/Suspicious Object Detection by Using VAE and NNS with Frame Difference Image
-
- YAMAMOTO Yoshiki
- OMRON SOCIAL SOLUTIONS CO.,LTD.
-
- SAKAI Shun
- OMRON SOCIAL SOLUTIONS CO.,LTD.
Bibliographic Information
- Other Title
-
- フレーム差分画像を用いたVAEと近傍探索による落下物・不審物検知
Description
<p>In recent years, with the growing need for a safe, secure, and comfortable environment, abnormal detection plays an important role to prevent terrorist attacks, incidents, and accidents, for example, detecting falling objects on the road or suspicious objects in facilities such as train stations. In previous work, the background subtraction method has been used to detect such objects. However, it has the problem of false detection of swaying grass and trees, changes in sunlight, etc. In this study, a new abnormal detection method is proposed that combines VAE (Variational Auto-Encoder) and NNS(Nearest Neighbor Search) using frame subtraction images to detect falling and suspicious objects from surveillance cameras. Experiment results show that for data 1 (Ayabe), the G-mean value was 0.975 by our proposed method, compared with 0.876 by the previously reported VAE and 0.663 by the background subtraction method using OpenCV. Furthermore, an incremental learning framework is constructed by feeding back the user’s classification result to reduce false detection. Experiment result on data 2 (Yasu) shows that the G-mean Value was improved by 0.072 with our method.</p>
Journal
-
- Proceedings of the Annual Conference of JSAI
-
Proceedings of the Annual Conference of JSAI JSAI2022 (0), 1F4GS1002-1F4GS1002, 2022
The Japanese Society for Artificial Intelligence
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390855656055827328
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed