Anomaly Detection and Localization in Crowded Scenes

By Wei-Xin Li, Vijay Mahadevan, Nuno Vasconcelos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014

1,101 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/aae932cf9c2434f52b03991fcab050a61a960d48

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Citations in the corpus

Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video (2018, background): “Abnormal event detection is commonly formalized as an outlier detection task [2, 5, 6, 9, 14, 15, 18, 23, 25, 26, 29, 36, 37, 38, 39], in which the main approach is to learn a model of familiarity from training videos and label the detected outliers as abnormal.”
Adversarially Learned One-Class Classifier for Novelty Detection (2018, background): “Such problems are especially of great interest in computer vision studies, as they are closely related to outlier detection [47, 52], image denoising [8], anomaly detection in images [10, 23] and videos [39].”
Spatio-Temporal Data Mining (2017, methodology): “…a land cover type) has been heavily studied in the remote sensing literature for a variety of problems [DeFries and Chan 2000; Jun and Ghosh 2011; Li et al. 2014b; Vatsavai 2008], e.g., the mapping of surface water dynamics using multi-spectral remote sensing data [Karpatne et al. 2016b;…”
Real-World Anomaly Detection in Surveillance Videos (2018, methodology): “Due to difficulties in obtaining reliable tracks, several approaches avoid tracking and learn global motion patterns through histogram-based methods [10], topic modeling [20], motion patterns [32], social force models [29], mixtures of dynamic textures model [27], Hidden Markov Model (HMM) on local ”
Crowded Scene Analysis: A Survey (2015, methodology): “Capturing Temporal [87] have shown that dynamic texture is more suitable for local unusual event detection in crowded scenes than optical ﬂow.”
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence (2019, methodology): “We performed experiments on various benchmark datasets of anomaly detection including CUHK Av-enue [26], UCSD Ped2 [24], Subway Entrance Gate and Exit Gate [1], Trafﬁc-Belleview and Trafﬁc-Train [52].”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, background): “Consequently, computer vision-based crowd analytics and surveillance [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] have received increased interest.”
Latent Space Autoregression for Novelty Detection (2018, methodology): “In this respect, prior works involve handcrafted features such as optical flow or trajectory analysis and, on top of that, employ both non-parametric [1] and parametric [5, 28, 22] estimators, as well as graphical modeling [16, 20].”
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning (2021, methodology): “Our model is evaluated on four multi-scene benchmark datasets, created for the weakly supervised video anomaly detection task: ShanghaiTech [28], UCF-Crime [57], XD-Violence [67] and UCSD-Peds [69].”
Learning Memory-Guided Normality for Anomaly Detection (2020, methodology): “Methods Ped2 [21] Avenue [24] Shanghai [26]”
HRN: A Holistic Approach to One Class Learning (2020, background): “Recently, image and video based applications have also become popular [13, 49, 70].”
Unmasking the Abnormal Events in Video (2017, background): “Abnormal event detection is usually formalized as an outlier detection task [2, 4, 5, 7, 9, 12, 13, 14, 15, 16, 20, 24, 25, 26], in which the general approach is to learn a model of normality from training data and consider the detected outliers as abnormal events.”
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.”