Abstract
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.
