Optimize Processes for Large Spaces with the Multi-Camera Tracking Workflow
Large areas like warehouses, factories, stadiums, and airports are typically monitored by hundreds of cameras to improve safety and optimize operations. Tracking objects and measuring activity accurately across these cameras is called multi-camera tracking, and it lets you effectively monitor and manage your spaces.
For example, retail stores can use multi-camera tracking to understand how customers navigate through the aisles and improve store layout for a better shopping experience. Warehouses can monitor the movement of equipment, material, and people to improve safety, increase delivery speed, and reduce costs. Airports can track the flow of people to enhance security and travel experience.
First, matching subjects across multiple camera feeds from different angles and views requires advanced algorithms and AI models that can take months to train accurately. In particular, ground-truth training datasets are scarce because labeling requires a single person or up to only a small group to review all the streams from numerous cameras for consistent identification and tracking, which delays AI model training.
Second, multi-camera tracking in real-time necessitates building specialized modules for live data streaming, multi-stream fusion, behavior analytics, and anomaly detection to deliver subsecond latency and high throughput.
