The Core Trade-off
Edge AI cameras run detection models on-device (in-camera chipsets), sending only metadata and alerts back to the network. Centralised analytics keeps cameras "dumb" and streams full video to a central server farm for processing. The choice affects bandwidth, latency, scalability and total cost very differently depending on the site profile.
When Edge Processing Wins
- Multi-site retail or F&B chains across the Emirates, where central bandwidth to a single control room is limited or costly
- Remote or logistics facilities with constrained connectivity back to a head office
- Deployments needing low-latency local response (a door lock triggered by on-camera detection) without round-trip network delay
When Centralised Processing Wins
Large single-site campuses — airports, stadiums, master developments — benefit from centralised analytics because cross-camera correlation (tracking a person or vehicle across dozens of cameras) is far more effective when processed together, and centralised GPU/NPU capacity can be scaled and upgraded independently of the camera hardware itself.
Hybrid Designs for Large UAE Portfolios
Many UAE master developments and multi-building estates end up with a hybrid: edge processing for simple, high-volume use cases (line-crossing, basic motion classification) at the camera, with select high-value zones (main entrances, perimeter, VIP areas) feeding full video to centralised analytics for more sophisticated correlation and forensic search. ASDV designs specify which zones get which tier explicitly, rather than a blanket approach that either overspends on edge hardware everywhere or overloads a central server.