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Conference Paper
Ensemble Grid Formation to Detect Potential Anomalous Regions Using Context Encoders
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- Issue Date
- 2018-10
- Citation
- International Conference on Control, Automation and Systems (ICCAS) 2018, pp.661-665
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Unsupervised visual anomaly detection is currently one of the most challenging machine learning problems. Various techniques have been specifically developed for certain limited domains while some are more generic towards a broader scope of applications. This paper aims to investigate a novel solution for general anomaly detection in surveillance videos by modeling patterns and objects that appear normally in the videos and then using this model to detect the anomalous objects by exploiting image reconstruction methodologies. This approach is inspired by the recent progression in development of robust semantic in-painting techniques. For our experiments, Context Encoders are used for the said purpose. Context encoders are proven successful to reconstruct missing holes in images based on the non-hole parts. Our proposed methodology is semi-supervised which means it does not require an annotated dataset however the videos of cases containing normal scenes are required separately to train the system. Various experiments suggest that the proposed methodology can successfully locate potentially anomalous images from the normal ones. This paper discusses in depth the possibilities of adopting such systems for general anomaly detection, pros, cons as well as the limitations of the overall methodology.
- KSP Keywords
- Anomalous objects, Grid Formation, Image reconstruction, In-painting techniques, Semi-supervised, Surveillance video, anomaly detection, machine Learning