Trajectory-Based Anomalous Event Detection

By Claudio Piciarelli, Christian Micheloni, Gian Luca Foresti. IEEE transactions on circuits and systems for video technology (Print), 2008

536 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ce9edb785f28c81bd7c2864940ed001429178e1e

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In Signal Processing

Citations in the corpus

Detecting anomalous events in videos by learning deep representations of appearance and motion (2017, background): “Trajectories are widely used features for abnormal video event detection [26, 6], due to their ability of describing synthetically the dynamic information of foreground objects.”
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence (2019, background): “These studies aim to learn patterns of object trajectories determined from normal events [34, 3, 36, 53].”
Advanced Structured Prediction (2014, background): “Convex relaxations of combinatorial problems, as appearing in computer vision, processing of medical data or analysis of transport networks, often contain millions of variables and hundreds of thousands of constraints.”
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization (2021, background): “In this case the system tries to learn a “normality” model from the training data and thus subsequently classify new samples as anomaly if they don’t fit the model [1], [16], [12], [24].”
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (2021, methodology): “Feature extraction followed by a normality modeling was a popular paradigm in early works [40, 1, 20].”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, methodology): “By using accurate tracking algorithms, trajectory extraction can be carried out to further perform trajectory clustering analysis [5, 20] or design representative features [28] to model typical activities and subsequently discover anomalies.”
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder (2017, background): “Trajectories have long been popular in video analysis and anomaly detection [34, 12, 20, 17].”
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes (2016, unknown): “Object trajectory estimation is often of interest in cases of anomaly detection; see [18, 22, 23, 24, 25, 26, 27, 28, 29].”