Real-Time Person Detection and Description Systems

Advancements in surveillance technologies are pivotal in addressing the growing demands for real-time monitoring and actionable insights across industries such as security, public safety, and traffic management.This review explores existing research and technologies in real-time person detection, attribute analysis, and semantic description generation, highlighting their applications, limitations, and gaps.It further presents a novel system integrating YOLOv8 for efficient object detection, OpenCV for targeted attribute analysis, and Qwen2VL for rich vision-language understanding, coupled with Sentence-BERT for semantic search.This integrated approach addresses key challenges including latency, scalability in processing complex queries, and dynamic environment adaptability.We discuss the technical advantages, computational considerations, particularly for scaling VLMs, and challenges in complex scenarios like dense crowds.Future directions include integrating emotion recognition, multilingual capabilities, and more sophisticated predictive analytics to enhance system utility and proactive security measures.