6 Problems with Traditional Video Surveillance and How AI Can Solve Them

Traditional video surveillance systems have several limitations in modern environments. One major issue is that human operators cannot effectively monitor numerous cameras at once. With video streams from dozens, hundreds, or even thousands of cameras, relying solely on human attention becomes impractical for detecting crucial events. Instead, AI-assisted monitoring can analyze camera feeds and identify potentially significant situations, providing alerts for further action.

Another challenge is the overwhelming amount of video footage generated by surveillance cameras. Most of the captured footage does not warrant immediate human attention. Traditional systems require operators to either continuously monitor live feeds, manually review recordings after incidents, or search through hours of video to locate relevant moments, which is inefficient and time-consuming.

AIVision addresses this issue by turning video into actionable events. It extracts frames from live streams, submits them to vision models for analysis, and associates the results with the stream and timestamp. This approach enables applications to automatically identify important events, store the analysis output, and retrieve relevant detections through APIs, allowing operators to focus on significant incidents rather than constantly watching video.

Traditional video analytics are also difficult to customize due to the prevalence of specialized models for specific detection tasks. For example, helmet detection requires a dedicated PPE detection model, while fire detection needs a separate fire and smoke detection model. When surveillance requirements change or vary across industries, developing, integrating, deploying, and maintaining multiple models becomes complex and resource-intensive.

Vision Language Models (VLMs) offer a more flexible solution. Instead of building separate detection pipelines for each question, applications can simply describe their desired insights using natural language prompts. This allows for easier customization and adaptation to various surveillance needs without the need for extensive model development.