Abnormal Event Detection at 150 FPS in MATLAB

By Cewu Lu, Jianping Shi, Jiaya Jia. IEEE International Conference on Computer Vision, 2013

1,333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/869b17632ed4f19f93b3b58dcaa9f0b8e92108f3

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Citations in the corpus

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies (2023, background): “Applications often have specific characteristics, such as the availability of image sequences in surveillance datasets [31, 36, 71] or the different modalities of medical imaging datasets (MRI [5], CT [3], X-ray [27], etc.”
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video (2018, methodology): “The samples are selected from the Avenue [23], the ShanghaiTech [24], the UCSD Ped2 [25] and the UMN [26] test videos, and are not seen during training the auto-encoders.”
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection (2021, methodology): “We integrate SSPCAB into various state-of-the-art anomaly detection frameworks [18, 34, 37, 39, 49, 79] and conduct comprehensive experiments on the MVTec AD [5], Avenue [40] and ShanghaiTech [43] data sets.”
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (2016, positive): “Lu et al. [44] propose a fast (140-150 fps) anomaly detection method based on sparse representation.”
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, methodology): “In CUHK Avenue Dataset [10] anomalies correspond to strange actions such as a person throwing papers or bag, moving in unusual directions, and appearance of unusual objects like bags and bicycle.”
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.”
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection (2019, background): “Sparse reconstruction [41, 31, 13, 67] is also another popular approach for usual pattern modeling.”
Learning Temporal Regularity in Video Sequences (2016, methodology): “We train our models using multiple datasets including CUHK Avenue [8], Subway (Enter and Exit) [11], and UCSD Pedestrian datasets (Ped1 and Ped2) [12], without compensating the dataset bias [13].”
Real-World Anomaly Detection in Surveillance Videos (2018, methodology): “Similar to [28], reconstruction error is used Testing videos Nu mb er o f fr am es”
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (2019, methodology): “Specifically, sparse representation methods [25, 45] jointly learn a dictionary and the sparse representation of the normal data for detecting anomalies.”
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder (2017, methodology): “We train our model on ﬁve most commonly used benchmarking datasets: Av-enue [13], UCSD Ped1 and Ped2 [14], Subway entrance and exit datasets [1].”
Future Frame Prediction for Anomaly Detection - A New Baseline (2017, methodology): “Besides these statistic models, sparse coding or dictionary learning is also a popular approach to encode the normal patterns [38][20][6].”
Fast and accurate image upscaling with super-resolution forests (2015, background): “Very recently, two different approaches came up to approximate sparse coding, aiming at much faster inference for different applications [26, 32].”
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos (2019, methodology): “We evaluate our method on two datasets for video anomaly detection: ShanghaiTech Campus [20] and CUHK Avenue [19].”
Latent Space Autoregression for Novelty Detection (2018, background): “Traditional sparse-coding algorithms [45, 9, 24] adhere to such framework, and represent normal patterns as a linear combination of a few basis components, under the hypotheses that novel examples would exhibit a non-sparse representation in the learned subspace.”
Spatio-Temporal AutoEncoder for Video Anomaly Detection (2017, methodology): “STAE-optflow takes the optical flow calculated by the algorithm described in [19] as input, the optical flow data provides discriminative temporal features and improves the performance of the STAE model on CUHK Avenue dataset.”