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Author/Affiliation: Idir BoulfrifiOpen AccessArticle
6 Pages, 1,136 KB 
Video Risk Detection and Localization using Bidirectional LSTM Autoencoder and Faster R-CNN
Abstract:
This work proposes a new unsupervised learning approach to detect and locate the risks “abnormal event” in video scenes using Faster R-CNN and Bidirectional LSTM autoencoder. The approach proposed in this work is carried out in two steps: In the first step, we used a bidirectional LSTM autoencoder to detect the frames containing risks. In…
Read More(This article belongs to Section Artificial Intelligence in Computer Science (CAI))