PERSON RE-IDENTIFICATION BASED ON DEEP LEARNING NETWORKS: A SURVEY

Person search: a comprehensive survey on image-based person search using deep learning

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

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.

Deep Learning-Based Face Detection, Feature Extraction, and Face Recognition from Video: A Comprehensive Review

A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.

Developing an Advanced Deep Learning SSD Algorithm Using Computer Vision Approaches to Enhance Vehicle Detection

The development of Computer vision and Deep learning frameworks plays a crucial role in Autonomous driving and real-time intelligent traffic surveillance. The Single Shot Detector (SSD) architecture is well known for its ability to perform inference quickly, and generically configured anchors are problematic because of the severe localization errors they cause. This article proposes an optimized architecture for SSD-based vehicle detection that addresses these challenges in two main steps: (1) using an optimized edge-preserving median filter to remove semantic noise in the image caused by the sensors while preserving the sharpness of the image boundaries and (2) applying custom-scaled anchor box aspect ratios specifically designed to capture the real size of the vehicles on the road. Applied to the standard KITTI Vision Benchmark dataset, the proposed framework's Mean Average Precision (mAP) and recall were 95.90% and 96.60%, respectively. The proposed model is compared with the baseline networks and is shown to significantly outperform them, achieving mAP 89.50% for vanilla SSD300 and 94.10% for YOLOv5. The proposed method reached high reliability with a balanced precision-recall rate that is highly suitable for real-time traffic monitoring systems.

Occluded person re-identification: a taxonomic survey and reproducible empirical benchmark

Advances in Deep Learning and Computer Vision for Sewer Inspection: A State-of-the-Art Review

This review provides a state-of-the-art survey of image-based automation techniques for sewer inspection, focusing on the progression from traditional rule-based image processing to advanced deep learning models. The core computer vision tasks are examined, including defect classification, object detection, and semantic segmentation, with an emphasis on their capabilities and limitations. Recent trends, such as the use of multilabel classification to detect multiple defects in a single image and transformer-based models for better understanding of visual context, are also discussed. The review also explores the emerging role of three-dimensional (3D) point cloud analysis and 3D reconstruction techniques in enhancing spatial understanding and defect localization. Despite notable advances, several challenges remain, such as the scarcity of standardized datasets, limited real-world validation, and the absence of fully automated deployable systems. To address these gaps, the paper recommends promoting open-access benchmark datasets; encouraging the open sharing of code and models; the adoption of data-efficient learning approaches such as unsupervised, semisupervised, self-supervised, and transfer learning to reduce reliance on large annotated datasets; the use of lightweight and deployable model architectures; and the validation of AI-driven models in real-world sewer inspection environments. These directions are essential to transforming current research innovations into practical, intelligent inspection systems.