A study on human abnormal activity detecting in intelligent video surveillance

Prabha, B.; Shanker, N. R.; Priya, M.; Ganesh, E.

doi:10.1063/5.0111875

Cited by 1 publication

(1 citation statement)

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“…Incidences of irregular conduct within these confined spaces-ranging from door picking, jumping, kicking, door blocking, inadvertent falls, or misplaced items to deliberate acts of vandalism-can precipitate safety risks. Such behaviors not only endanger the well-being of passengers but also compromise the integrity of the elevator system [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

Lei,

Sun,

Fang

et al. 2024

Electronics

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In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively.

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Section: Introductionmentioning

confidence: 99%