Object and patch based anomaly detection and localization in crowded scenes

2019 ◽  
Vol 78 (15) ◽  
pp. 21375-21390
Author(s):  
Xiaodan Li ◽  
Weihai Li ◽  
Bin Liu ◽  
Nenghai Yu
Keyword(s):  
Anomaly Detection ◽  
Detection And Localization ◽  
Crowded Scenes

scholarly journals Anomaly Detection and Localization in Crowded Scenes

2014 ◽  
Vol 36 (1) ◽  
pp. 18-32 ◽  
Author(s):  
Weixin Li ◽  
Vijay Mahadevan ◽  
Nuno Vasconcelos
Keyword(s):  
Anomaly Detection ◽  
Detection And Localization ◽  
Crowded Scenes

Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

2017 ◽  
Vol 26 (4) ◽  
pp. 1992-2004 ◽  
Author(s):  
Mohammad Sabokrou ◽  
Mohsen Fayyaz ◽  
Mahmood Fathy ◽  
Reinhard Klette
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Deep Neural Networks ◽  
Detection And Localization ◽  
Crowded Scenes

Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes

2016 ◽  
Vol 47 ◽  
pp. 358-368 ◽  
Author(s):  
Shifu Zhou ◽  
Wei Shen ◽  
Dan Zeng ◽  
Mei Fang ◽  
Yuanwang Wei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Anomaly Detection ◽  
Convolutional Neural Networks ◽  
Detection And Localization ◽  
Crowded Scenes

Anomaly detection and localization in crowded scenes using connected component analysis

2017 ◽  
Vol 77 (12) ◽  
pp. 14767-14782 ◽  
Author(s):  
Somaieh Amraee ◽  
Abbas Vafaei ◽  
Kamal Jamshidi ◽  
Peyman Adibi
Keyword(s):  
Anomaly Detection ◽  
Component Analysis ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Detection And Localization ◽  
Crowded Scenes

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

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