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.