September 25, 2025INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTOpen Access

Detection and Localization of Anomalies in Crowded Environments

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Authors

SSS SumanthCarleton University PKPoornima KulkarniUniversity of Mysore

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Implication

This project demonstrates an anomaly detection system utilizing optical flow in surveillance video, suggesting efficiency in monitoring crowded environments.

Key Points

The system effectively identifies anomalies in human movement, providing visual feedback in real-time using color-coded overlays.
It incorporates a decision tree classifier trained on labeled data to improve the identification of abnormal behaviors.
Optimized for edge deployment, this system can run on resource-limited devices like laptops and Raspberry Pi.
Future enhancements may focus on incorporating deep learning techniques for more robust performance.

Cite This Study

Sumanth et al. (2025) studied this question.

synapsesocial.com/papers/68d5e205bd7882ccb9b867de https://doi.org/10.55041/ijsrem52730

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Implication

This project demonstrates an anomaly detection system utilizing optical flow in surveillance video, suggesting efficiency in monitoring crowded environments.

Detection and Localization of Anomalies in Crowded Environments

Discussion

Member takes

Implication

Key Points

Cite This Study

Detection and Localization of Anomalies in Crowded Environments

Discussion

Member takes

Implication

Key Points

Cite This Study

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