Suspicious Human Behaviour Detection Focusing on Campus Sites

Ali, Mohammed Mahmood; Noorain, Sara; Qaseem, Mohammad S.; Rahman, Ateeq Ur

doi:10.1007/978-981-19-2940-3_12

Cited by 3 publications

(1 citation statement)

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“…They are expected to view many screens recorded by different cameras, often simultaneously. Generally, classification is one of the most important and difficult steps in image processing [10,11]. Various methods have been used to recognize human behavior from images, but some of these methods do not show optimal performance in some situations, and in some cases, the methods used are either not able to detect people under certain conditions or do not have high accuracy [12,13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Ya-meng¹,

Wang²,

Shen-shen³

2022

IJACSA

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The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

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