Amneen: An AI-Based System for Real-Time Tracking and Management of Crowds

Intelligent Crowd Analysis: A Learning-Based Approach for Detection and Behavior Analysis in Surveillance Videos

This work presents a novel Intelligent Crowd Analysis (ICA) system in this paper that takes advantage of a Modified YOLOv4-tiny object detection model with Non-Maximum Suppression (NMS), Deep SORT object tracking and a Crowd Monitoring and Behavior Analysis (CMBA)module that is designed by us.

An AI-based real-time framework for multi-behavior detection and tracking of Mithun (Bos frontalis) using YOLOv8n and deepSORT

Mithun (Bos frontalis), a culturally and economically significant semi-domesticated bovine species native to the hilly regions of Northeast India, plays a vital role in tribal livelihoods and regional ecosystems. Traditional methods for monitoring Mithun behavior rely on manual observation, which is labor-intensive, time-consuming, and unsuitable for continuous surveillance. To address this limitation, we propose the first artificial intelligence-based, real-time, non-contact framework for automatic behavior detection and tracking of Mithun in natural farm environments. The system integrates the YOLOv8n object detection model with the DeepSORT tracking algorithm to recognize four key behaviors: feeding, standing, lying, and mounting. A custom dataset of 3,000 manually annotated images was developed from high-resolution CCTV footage captured under varied lighting and environmental conditions, including infrared night vision. The trained model demonstrated strong performance with a mean average precision (mAP@0.5) of 0.995, a recall of 0.996, and a real-time inference speed of 31 frames per second. Extensive quantitative evaluations, confusion matrix analysis, and qualitative visualizations confirmed the model’s robustness under occlusion, low light, and background clutter. DeepSORT was incorporated to support qualitative identity-aware tracking across frames; however, tracking performance was not quantitatively evaluated. This scalable, non-invasive framework offers significant potential for improving reproductive management, enhancing animal welfare, and enabling continuous precision livestock monitoring, particularly in remote or resource-constrained tribal farming systems.

Traditional ML-based analysis of anomalous crowd behavior: A thorough review

Automatic traffic surveillance system leveraging generative AI large language models

A generative AI-based traffic surveillance system leveraging large language models (LLMs) to enable timely and context-rich interpretation of traffic events, demonstrating the system's effectiveness in producing context-aware traffic scene descriptions, improving operational decision-making, and enhancing roadway safety.

turnitin unesa1

Intelligent Crowd Monitoring and Early Warning Framework for Stampede Prevention Using YOLOv8m and CNN-LSTM Architecture

Railway stations, religious events, and other large public gatherings are prone to overcrowding, sometimes resulting in stampede situations. The existing systems for monitoring crowds primarily rely on manual monitoring, which prevents them from producing early warnings for potentially dangerous situations. In this paper, the authors describe a machine vision-enabled crowd monitoring and early warning framework that is based on YOLOv8m, CNN, and LSTM models with the ability to perform real-time analysis of crowds for the purpose of predicting the risk of a stampede. The framework uses real-time video streams from surveillance cameras for the processes of crowd detection, density estimation, motion analysis, flow direction tracking, and prediction of temporal behaviour. Results demonstrate that the proposed framework successfully identifies abnormal crowd behaviour and predicts the potential for extreme or dangerous situations prior to their occurring. The proposed system enhances overall crowd safety within a high-density area, enhances the accuracy of detecting abnormal behaviour, and improves the ability to provide early warning capability of a potentially dangerous crowd situation.