Video anomaly detection based on local statistical aggregates

By Venkatesh Saligrama, Zhu Chen. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/785a7e01267683deb54a94f6a8b4019806e3f085

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Citations in the corpus

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (2016, neutral): “The proposed method gains better outcomes in terms of EER for all methods except Saligrama et al. [42]; they achieve better results by 0.3%.”
Detecting anomalous events in videos by learning deep representations of appearance and motion (2017, methodology): “9% Local Statistical Aggregates [2] 92.”
Abnormal event detection in videos using generative adversarial nets (2017, methodology): “However, despite a lot of research has been done in this area in the past years [1, 2, 3, 4, 5, 6, 7], the problem is still open.”
Unmasking the Abnormal Events in Video (2017, methodology): “with several state-of-the-art methods that require training data [5, 9, 13, 14, 15, 16, 18, 20, 24, 25].”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, methodology): “Opposite to previous works [8, 21, 24], instead of using hand-crafted features to model activity patterns, we propose to learn discriminative feature representations of both appearance and motion patterns in a fully unsupervised manner.”
Anomaly detection in dynamic networks: a survey (2015, methodology): “Vertex detection [55, 56, 67, 106] Subgraph detection [3, 13, 30, 31] Change detection [38] Edge detection [22] Change detection [112] Vertex detection [129] Event detection [68, 72, 75, 77, 83, 84, 93, 113, 114, 115, 122] Change detection [7, 65, 66] Edge detection [1, 80] Subgraph detection [60, 8”
Anomaly Detection and Localization in Crowded Scenes (2014, methodology): “This achieves the best result among all methods, outperforming the sparse reconstruction of [8] and the local statistical aggregates of [23].”
Spatio-Temporal AutoEncoder for Video Anomaly Detection (2017, background): “has drawn increased attention from both academia and industry in the last a few years [2, 3, 12, 23, 32].”
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].”
Abnormal Event Detection at 150 FPS in MATLAB (2013, methodology): “SF-MPPCA [13] SF [13] MDT [13] Sparse[5] Saligrama [16] Antic [2] Subspace [7] Ours EER 40 % 31 % 25 % 19 % 16 % 18 % 29.”
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, methodology): “One could cite work on anomaly detection based on K-Nearest Neighbors [27], unsupervised clustering [28], and object speed and size [29].”
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video (2018, methodology): “We evaluate our approach in comparison with a series of state-of-the-art methods [6, 7, 9, 11, 12, 13, 15, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 36, 37, 38] on the Avenue, the ShanghaiTech, the UCSD Ped2 and the UMN data sets.”