Granular trajectory based anomaly detection for surveillance

Maiorano, Francesco; Petrosino, Alfredo

doi:10.1109/icpr.2016.7899940

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…It had the advantage of being relatively robust, even in the presence of noise or outliers, but had the disadvantages of high time complexity and requiring additional parameters, such as time. Maiorano et al [51] employed the existing rough set extraction approach (ROSE) [52] on the points sampled by the slicing window. The number of the outlier points detected by ROSE in the test trajectory was used as the outlier score of the trajectory.…”

Section: Detectors Using Sampling-based Pre-processing Methodsmentioning

confidence: 99%

MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors

2022

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Trajectory outlier detection is one of the fundamental data mining techniques used to analyze the trajectory data of the Global Positioning System. A comprehensive literature review of trajectory outlier detectors published between 2000 and 2022 led to a conclusion that conventional trajectory outlier detectors suffered from drawbacks, either due to the detectors themselves or the pre-processing methods for the variable-length trajectory inputs utilized by detectors. To address these issues, we proposed a feature extraction method called middle polar coordinates (MiPo). MiPo extracted tabular features from trajectory data prior to the application of conventional outlier detectors to detect trajectory outliers. By representing variable-length trajectory data as fixed-length tabular data, MiPo granted tabular outlier detectors the ability to detect trajectory outliers, which was previously impossible. Experiments with real-world datasets showed that MiPo outperformed all baseline methods with 0.99 AUC on average; however, it only required approximately 10% of the computing time of the existing industrial best. MiPo exhibited linear time and space complexity. The features extracted by MiPo may aid other trajectory data mining tasks. We believe that MiPo has the potential to revolutionize the field of trajectory outlier detection.

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Section: Detectors Using Sampling-based Pre-processing Methodsmentioning

confidence: 99%

MiPo: How to Detect Trajectory Outliers with Tabular Outlier Detectors

2022

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“…Ref. [8] proposed an online tool to detect the trajectory points in real time and detect the outliers as normal or abnormal. The proposed method used the video frame's temporal window and the threshold value of a trajectory to classify it as normal or abnormal.…”

Section: Related Workmentioning

confidence: 99%

Crowd Density Level Estimation and Anomaly Detection Using Multicolumn Multistage Bilinear Convolution Attention Network (MCMS-BCNN-Attention)

Ekanayake

Lei

2022

Applied Sciences

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The detection of crowd density levels and anomalies is a hot topic in video surveillance. Especially in human-centric action and activity-based movements. In some respects, the density level variation is considered an anomaly in the event. Crowd behaviour identification relies on a computer-vision-based approach and basically deals with spatial information of foreground video information. In this work, we focused on a deep-learning-based attention-oriented classification system for identifying several basic movements in public places, especially, human flock movement, sudden motion changes and panic events in several indoor and outdoor places. The important spatial features were extracted from a bilinear CNN and a multicolumn multistage CNN with preprocessed morphological video frames from videos. Finally, the abnormal and crowd density estimation was distinguished by using an attention feature combined with a multilayer CNN feature by modifying the fully connected layer for several categories (binary and multiclass). We validate the proposed method on several video surveillance datasets including PETS2009, UMN and UCSD. The proposed method achieved an accuracy of 98.62, 98.95, 96.97, 99.10 and 98.38 on the UCSD Ped1, UCSD Ped2, PETS2009, UMN Plaza1 and UMN Plaza2 datasets, respectively, with the different pretrained models. We compared the performance between recent modern approaches and the proposed method (MCMS-BCNN-Attention) and achieved the highest accuracy. The anomaly detection performance on the UMN and PETS2009 datasets was compared with that of a state-of-the-art method and achieved the best AUC results as 0.9953 and 1.00 for both scenarios, respectively, with a binary classification.

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