Unsupervised and Adaptive Perimeter Intrusion Detector
Devashish Lohani, Carlos Fernando Crispim-Junior, Quentin Barthélemy, Sarah Bertrand, Lionel Robinault, Laure Tougne Rodet · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
Perimeter intrusion detection (PID) deals with the detection of intruders displacing in a protected perimeter. In the video surveillance domain, deep learning has shown tremendous progresses. Existing deep learning based PID systems (PIDS) are supervised and thus require a lot of annotated data. However, since intrusions are rare events, there are very few positives in datasets, thus making them highly imbalanced. Furthermore, a PIDS must adapt to varying real-life scene dynamics, like weather, light, environmental conditions, etc. To address these issues, we propose an autoencoder-based, end-to-end trainable, unsupervised PIDS with a module that can adapt to long-term variations in scene dynamics. Our results show competitive performance of the proposed system on the standard i-LIDS dataset.