Multi-Dimensional Optical Flow Embedded Genetic Programming for Anomaly Detection in Crowded Scenes

Mi, Ze-Yu; Shang, Lin; Xue, Bing

doi:10.1007/978-3-030-04179-3_43

Cited by 2 publications

(1 citation statement)

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“…To recognize the certain anomalies, the model of anomaly detection, for example, traffic accident and violence detector has been introduced. To recognize the anomalies, it is noted that the solutions might not be in discriminate and they are restricted to limited usage [2]. Few real time anomalies are distinct and composite, when it is necessary to denote each anomaly.…”

Section: Introductionmentioning

confidence: 99%

Mask Region Based Convolution Neural Network (R-CNN) based Smart System for Anomaly Detection in Pedestrian Walkways

Nirmala*,

Arivalagan,

Sudhakar

2019

IJRTE

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Recently, anomaly detection becomes a fascinating research application which usually raises an alarm in scenarios where the event varies from the actual event. Anomaly detection can be treated as a coarse-level video understanding problem that determines the existence of anomalies from habitual events. This paper introduces a new anomaly detection model by the use of Mask region based convolution neural network (R-CNN). The application of mask in the detection process helps to precisely identify the presence of anomalies in the scene. The effectiveness of the Mask R-CNN based anomaly detection model is validated against UCSD anomaly detection dataset. An extensive quantitative and experimental outcome evidently shows the superior nature of the presented model over the compared methods in a significant manner.

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Section: Introductionmentioning

confidence: 99%