VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface

• URL: http://arxiv.org/abs/2203.01193v1
• Date: Wed, 2 Mar 2022 15:47:36 GMT
• Title: VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface
• Authors: Takato Yasuno, Junichiro Fujii, Riku Ogata, Masahiro Okano
• Abstract summary: In road monitoring, it is important to detect changes in the road surface at an early stage to prevent damage to third parties. We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In road monitoring, it is an important issue to detect changes in the road surface at an early stage to prevent damage to third parties. The target of the falling object may be a fallen tree due to the external force of a flood or an earthquake, and falling rocks from a slope. Generative deep learning is possible to flexibly detect anomalies of the falling objects on the road surface. We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector in application for road surface monitoring. Actually, we apply our method to a set of test images that fallen objects is located on the raw inputs added with fallen stone and plywood, and that snow is covered on the winter road. Finally we mention the future works for practical purpose application.

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