Learning object motion patterns for anomaly detection and improved object detection
By Arslan Basharat, Alexei Gritai, Mubarak Shah. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008
411 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/08f99af9f5d6d351201ee4563e407bf37bc164fd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Eric P. Xing
- Mubarak Shah
- Nuno Vasconcelos
- Li Fei-Fei
- Jean Meunier
- Trong-Nguyen Nguyen
- Kristen Grauman
- Rita Cucchiara
In Biomedical Engineering
- Jean Meunier
- Trong-Nguyen Nguyen
- Guansong Pang
- Kristen Grauman
- Waqas Sultani
- Mubarak Shah
- Li Fei-Fei
- Gustavo Carneiro
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Kristen Grauman
- Xiaogang Wang
- Ming-Hsuan Yang
- Jitendra Malik
- Yi Yang
- Li Fei-Fei
Citations in the corpus
- 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].”
- Online detection of unusual events in videos via dynamic sparse coding (2011, background): “In this paper, we define unusual events as those incidences that occur very rarely in the entire video sequence [1, 7, 22, 3, 21, 14].”
- 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].”
- 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].”
- Anomaly detection in crowded scenes (2010, background): “It comprises tracking each object in the scene, and learning models for the resulting object tracks [5, 22, 24].”
- A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016, background): “For example, surveillance camera data can be analyzed for suspicious movements [10], in smart buildings energy consumption anomalies can be found [11], mobile communication networks can be monitored [12] and also forged documents can be detected by a forensic application investigating printed docume”
- Real-World Anomaly Detection in Surveillance Videos (2018, background): “Beyond violent and non-violent patterns discrimination, authors in [39, 7] proposed to use tracking to model the normal motion of people and detect deviation from that normal motion as an anomaly.”
- Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning (2021, background): “Traditional anomaly detection methods assume the availability of normal training data only and address the problem with one-class classification using handcrafted features [2, 32, 64, 75].”
- Anomaly Detection and Localization in Crowded Scenes (2014, background): “For example, [6] and [1] adopt Gaussian mixture models (GMM) to represent trajectories of normal behavior.”
- Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates (2009, background): “Tracking-based algorithms [2, 14, 9] determine the abnormality for each object’s trajectory.”
- Enhancing one-class support vector machines for unsupervised anomaly detection (2013, background): “Applications that utilize anomaly detection include intrusion detection [22], medical diagnosis [17], fraud detection [29] and surveillance [3].”