Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

By Mohammad Sabokrou, Mahmood Fathy, Reinhard Klette. IEEE Transactions on Image Processing, 2017

375 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6f68ce1e03c56c186256dac689a21f6405ae8d96

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Citations in the corpus

Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning (2021, background): “Al-ternatively, some approaches depend on data reconstruction using generative models to learn the representations of normal samples by (adversarially) minimising the reconstruction error [6,14,18,18,28,35–37,42,49,52,53,62,68,82].”
Adversarially Learned One-Class Classifier for Novelty Detection (2018, methodology): “, trajectories [29]), deeply learned features [46, 37, 38] are used in the literature.”
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (2016, positive): “Compared to the previously published deep-cascade method in [1], this paper proposes and evaluates a different and new method for anomaly detection.”
HRN: A Holistic Approach to One Class Learning (2020, background): “Recently, image and video based applications have also become popular [13, 49, 70].”
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018, background): “Much of the recent work on this problem domain pays particular attention to the use of spatiotemporal features by modeling spatiotemporal CNN [22], 3D convolutional autoencoders [23, 24] and long short-term memories [25].”
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (2020, methodology): “While the early works [1, 2, 6, 25, 29, 32, 33, 46, 58] on video anomaly detection relied heavily on handcrafted appearance and motion features, the recent literature is abundant in deep learning methods [9, 10, 14, 16, 17, 27, 31, 38, 40, 41, 44, 47, 54, 59, 60].”
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, methodology): “A rejection cascade over spatio-temporal cubes was generated to improve the performance speed of Deep-CNN based video anomaly detection framework by authors in [53].”
AnomalyNet: An Anomaly Detection Network for Video Surveillance (2019, methodology): “We compare our method with some state-of-the-art approaches including two handcrafted features based methods [7], [66] and six deep learning based methods [3], [5], [14], [34], [37], [39], [64] on these two datasets.”
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection (2021, methodology): “Anomaly detection methods can be classified into: dictionary learning methods [7–9, 14, 40, 55], change detection frameworks [11, 26, 38, 48], probability-based methods [1, 2, 16, 23, 29, 44, 45, 56, 61, 74], distance-based models [6, 10, 25, 27, 50, 51,53,57,58,63,65,68,70], and reconstruction-base”
Learning Memory-Guided Normality for Anomaly Detection (2020, background): “DeepCascade [37] detects various normal patches using cascaded deep networks.”
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video (2018, methodology): “Although auto-encoders have been used before for abnormal event detection [11, 31, 37], to our knowledge, we are the first to train object-centric auto-encoders.”