Live Surveillance with Actionable Intelligence

The combination of advanced machine learning and AI capabilities associated with natural language processing (NLP) and technological developments in computer vision have led to the creation of smart video surveillance systems. Unlike conventional Closed Circuit Television (CCTV) and motion-detection-based surveillance devices, these systems are capable of understanding contextual information, reducing false alarms and requiring less human intervention. Researchers have explored incorporating vision-language models (VLMs) and Sentiment Analysis (SA) into video surveillance applications to improve contextual awareness. This review focuses on emerging techniques associated with image captioning models, including Salesforce\\\'s BLIP, used to generate natural language descriptions of real-time actions in video footage and perform SA on those descriptions to determine the nature of the detected activity. By utilizing visual comprehension, context building, and sentiment interpretation, surveillance systems can differentiate between normal and suspicious behavior while reducing false positives and generating actionable insights. Applications include public safety in smart cities, security at high-threat locations such as airports and banks, and monitoring of sensitive areas including hospitals and military installations. This review evaluates how contextual awareness enabled by VLM improves traditional object detection methodologies and supports the transition toward more human-like and explainable alerting modalities. It also discusses limitations related to computational burden, accuracy, and privacy, while highlighting broader societal implications aligned with Sustainable Development Goals focused on urban safety and crime reduction. Future research directions include multimodal fusion, real-time optimization, and ethical guidelines for responsible deployment.