LAUSR

In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies. Indeed, for real-world problems, the representation of the normal class in the feature space is an important issue, considering that one or more clusters can describe different aspects of the normality. For classification purposes, it is important that these clusters be as compact (dense) as possible, for better discriminating anomalous patterns, which is a recurrent problem in OCC tasks. This work introduces a hybrid approach using deep learning and One-Class Support Vector Machine (OC-SVM) methods, named Convolutional Autoencoder with Compact Embedding (CAE-CE), for enhancing the compactness of clusters in the feature space. Such an approach is still underexplored in the literature, being restricted to models within the context of metric learning. Additionally, the absence of anomalous samples during training makes it difficult to determine when to interrupt the learning process, so as to avoid over-compression of the normal examples, thus resulting in overfitting of the model. In this work, we propose a novel sensitivity-based stop criterion, and its suitability for OCC problems was assessed. Using an OC-SVM for the classification task, several experiments were done using publicly available image and video datasets. We also introduce other two new benchmarks, specifically designed for video anomaly detection in highways. The final performance of the proposed method was compared with a baseline Convolutional Autoencoder (CAE). Overall results suggest that the enhanced compactness introduced by the CAE-CE improved the classification performance for most datasets. Also, the qualitative analysis of frames at the visual level indicated that features learned by CAE-CE are closely correlated to the anomalous events.