Unusual behavior detection and object ranking from movement trajectories in target regions

Barragana, Mateus de Oliveira; Älvares, Luís Otávio; Bogorny, Vânia

doi:10.1080/13658816.2016.1202415

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…For instance, dangerous behaviors between pedestrians and roads include crossing the road border, illegal stay, road crossing, moving along the curb, and entering the road [33]. Unusual behaviors between moving objects and locations were classified as surround, escape, and return [34].…”

Section: Abnormal Behavior Detection Based On Trajectory Analysismentioning

confidence: 99%

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Cheng

Zhang

Chen

et al. 2022

IJGI

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Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.

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Section: Abnormal Behavior Detection Based On Trajectory Analysismentioning

confidence: 99%

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Cheng

Zhang

Chen

et al. 2022

IJGI

View full text Add to dashboard Cite

show abstract

“…This definition implies that the object is significantly different from the overall database as a whole. However, in case of spatiotemporal databases, it is possible for an object to appear consistent with the entire database objects, but appear unusual with a local neighborhood [17,18]. Therefore, we can say that an outlier is a spatiotemporally geo referenced object whose non-spatiotemporal attribute values differ from objects in its spatiotemporal neighborhood.…”

Section: B Extracting Semantic Outliersmentioning

confidence: 99%

A Semantic Interpretation of Unusual Behaviors Extracted from Outliers of Moving Objects Trajectories

Chakri¹,

Raghay²,

Hadaj³

2017

ijacsa

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Abstract-The increasing use of location-aware devices has led to generate a huge volume of data from satellite images and mobile sensors; these data can be classified into geographical data. And traces generated by objects moving on geographical territory, these traces are usually modeled as streams of spatiotemporal points called trajectories. Integrating trajectory sample points with geographical and contextual data before applying mining techniques can be more gainful for the application users. It contributes to produce significant knowledge about movements and provide applications with richer and more meaningful patterns. Trajectory Outliers are a sort of patterns that can be extracted from trajectories. However, the majority of algorithms proposed for discovering outliers are based on the geometric side of trajectories; our approach extends these works to produce outliers based on semantic trajectories in order to give meaning to the outliers extracted, and to understand the unusual behaviors that can be detected. To prove the efficiency of the approach proposed we show some experimental results.

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A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context

Huang

2022

International Journal of Applied Earth Observation and Geoinfor

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Unusual behavior detection and object ranking from movement trajectories in target regions

Cited by 6 publications

References 21 publications

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

A Semantic Interpretation of Unusual Behaviors Extracted from Outliers of Moving Objects Trajectories

A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context

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