Deep Feature Learning For Person Re-Identification In Large-Scale Railway Environments

Abstract

Keeping passengers secure and keeping an eye out for suspicious activity has become much more difficult due to the exponential expansion of public transport networks, especially the Indian Railways network. Manual monitoring is laborious and ineffective at large train stations due to the high volume of people using them. In response to these issues, the authors of this work provide a smart Person Re-Identification (Re-ID) system that detects and tracks suspicious persons across several surveillance cameras in real-time using deep learning algorithms. The accuracy of traditional surveillance methods is sometimes compromised when subjected to changes in illumination, camera angles, and crowd density due to their reliance on manually observed and hand-crafted picture attributes. On the other hand, the suggested method makes use of CNNs to autonomously train and extract discriminative characteristics related to body posture and facial expressions from video and picture frames. The accuracy of suspect tracking and identification performance are both improved by these deep characteristics. There are many main steps that the suggested framework follows. At the outset, the system is fed a database that includes photos and details on people who could be considered suspect. After that, the CNN model analyses the photos to get useful feature representations. Machine learning methods like Random Forest and Support Vector Machine (SVM) are taught using the retrieved characteristics to enhance detection performance and classification efficiency. The trained model is seamlessly incorporated into a real-time surveillance system, which constantly monitors live video feeds from railway cameras. When the system identifies a suspicious person in the surveillance video, it will immediately send out warnings and save the specifics of the occurrence in the database for future reference. Administrators keep the system running smoothly, managing databases and verifying alerts, while railway security staff may use the monitoring interface to see live feeds and examine detection findings. The implementation is carried out utilising MySQL for safe data storage and administration and Python for model creation. The Random Forest classifier outperforms competing models in terms of experimental assessment, which shows improved detection accuracy and reduced false alarms. Using sample surveillance footage, the system reliably and accurately identified suspects during testing. When it comes to bolstering security at Indian Railway stations, the suggested Person Re-Identification system, which is based on deep learning, shows to be both effective and scalable. Improving public safety and bolstering current intelligent surveillance infrastructure are both greatly assisted by the system\'s ability to automate suspect identification and enable continuous monitoring.

Introduction

This research proposes a Deep Learning–based Person Re-Identification (Re-ID) system to improve security and surveillance in Indian Railways by automatically identifying and tracking suspicious individuals across multiple CCTV cameras. Traditional person identification methods rely on manually designed image features, which perform poorly under changes in lighting, camera angle, body posture, occlusions, and facial expressions. To overcome these limitations, the proposed system uses Convolutional Neural Networks (CNNs) to automatically extract robust facial and body features, enabling more accurate and reliable person recognition in real-world environments.

The extracted deep features are classified using Support Vector Machine (SVM) and Random Forest algorithms to distinguish suspicious individuals from ordinary passengers by comparing live surveillance data with a preloaded suspect database. When a match is detected, the system generates real-time alerts containing detection time, captured frames, and suspect information, allowing railway security personnel to respond quickly.

The system consists of two main interfaces:

• Admin Panel: Allows administrators to manage employee accounts, upload suspect datasets, train CNN and machine learning models, maintain databases, and review alerts.
• Employee Panel: Enables security staff to monitor live or recorded CCTV footage, upload surveillance videos, receive automatic alerts, and track suspicious individuals in real time.

The implementation uses Python for application development and MySQL for secure data storage. An intelligent Alert Management System records all detections for future investigation and administrative review.

The literature survey highlights that traditional hand-crafted feature methods suffer from poor robustness, while CNN-based feature extraction significantly improves person re-identification accuracy. Combining CNNs with machine learning classifiers further enhances recognition performance across varying surveillance conditions. The system is specifically designed for railway security, providing continuous monitoring, automated suspect identification, and rapid response capabilities.

The proposed methodology integrates deep learning for feature extraction and hybrid machine learning classification to achieve higher detection accuracy than conventional approaches. By continuously analyzing CCTV footage, automatically matching individuals with a suspect database, and generating instant notifications, the system reduces manual monitoring effort and strengthens public safety.

Conclusion

Deep Learning\'s Person Re-Identification (Re-ID) system improved public safety at Indian railway stations by reliably and effectively completing identification tasks based on surveillance data. The system enhanced its accuracy in identifying suspicious persons across several camera perspectives and different video circumstances by using CNN-based feature extraction instead of typical handmade image characteristics. Effective human recognition and classification was made possible by combining SVM and Random Forest, two machine learning classifiers; the Random Forest model demonstrated better accuracy in experimental evaluations. Furthermore, the system\'s modular architecture allowed for effective administration of datasets, training of models, and real-time monitoring of surveillance, thanks to the distinct Admin and Employee features.As a whole, the suggested architecture improves public safety and monitoring efficiency in large-scale public spaces like Indian railway stations by providing a practical, intelligent, and scalable security solution.

References

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Copyright

Copyright © 2026 T. Bhagya Laxmi, G V Gopala Krishna Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.