Title: Learning Spatio-Temporal Features for Detecting Anomalies in Videos using Convolutional Autoencoder
Authors: Manassés Ribeiro, Marcelo Romero, André Lazzaretti, Heitor Silvério Lopes
Abstract: Automatic video surveillance systems are a recurrent topic in recent video analysis research. Anomaly detection is an interesting way for tackling this problem, because video analysis is tedious and exhaustive for humans. Depending on the application field, anomalies can present different characteristics and challenges for pattern representation, requiring the design of hand-crafted features (such as spatial and temporal information). Deep learning methods have achieved the state-of-the-art performance for many recognition problems in recent years and may be an interesting choice for learning features automatically, since it captures the 2D structure in image sequences during the learning process. The deep Convolutional Autoencoder (CAE) may be an interesting approach for anomaly detection since they can learn signatures automatically in an unsupervised way. This work purposes the use of a deep CAE in the anomaly detection context for learning spatio-temporal signatures from raw video frames. Similar our previous work, we use as anomaly score a successful strategy based on the reconstruction error of a package of frames. The proposed methods were evaluated by means of several experiments with public-domain datasets. The promising results support further research in this area.
Key-words: Anomaly detection; One-class classification problems; Convolutional autoencoders; Deep learning methods
DOI code: 10.21528/CBIC2019-140
PDF file: CBIC2019-140.pdf
BibTeX file: CBIC2019-140.bib