984 research outputs found
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Videos represent the primary source of information for surveillance
applications and are available in large amounts but in most cases contain
little or no annotation for supervised learning. This article reviews the
state-of-the-art deep learning based methods for video anomaly detection and
categorizes them based on the type of model and criteria of detection. We also
perform simple studies to understand the different approaches and provide the
criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum
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