Site cameras already exist. Video analytics turns them from a recording device into a sensor. Zone intrusion, personal protective equipment compliance, people and vehicle counting, dwell time and queue analytics — processed at the edge so that video does not leave the site and a decision does not wait on a network round trip.
Presence in a defined area — machine envelopes, loading bays, restricted zones — with dwell and persistence logic to suppress transient alarms.
Helmet, high-visibility clothing, eye and hearing protection detected at zone entry, with reporting that supports coaching rather than surveillance.
Directional counting at doors, gates and aisles, for occupancy, flow and utilisation.
How long people or vehicles remain in a zone, and how queues build, for service and throughput analysis.
Proximity between vehicles and people in shared aisles, one of the highest-severity risks in warehousing.
Fall detection in areas where a lone worker may be unattended for a long period.
Streaming continuous video from twenty cameras to a cloud service is expensive in bandwidth, fragile when the link degrades, and awkward under most data-protection regimes. Running inference at the edge inverts all three: only events and metadata leave the site, the system keeps working through an outage, and imagery of identifiable people need not be transmitted at all.
It also fixes the latency problem. A zone intrusion alert that has to survive a cloud round trip is not an interlock. One computed locally can be.
Usually, provided resolution, frame rate and placement suit the task. An edge inference node takes the existing stream, so cameras rarely need replacing — though placement sometimes does, because an angle chosen for recording is not always an angle that makes a behaviour observable.
No. Inference runs at the edge, and systems can be configured to emit only events and metadata, retaining no imagery at all. That removes most of the bandwidth cost and a large part of the privacy exposure.
By designing for aggregate and zone-based reporting rather than individual identification, and by agreeing retention and access with the workforce before deployment. Systems used for coaching are tolerated; systems used for individual discipline are defeated.
Mostly transient crossings, shadows, reflective surfaces and occlusion. Persistence logic, zone geometry chosen against real traffic patterns and tracking across frames handle the majority.
Tell us the specification, the constraint and the deadline. Programmes that cross silicon, radio, embedded and AI are where Faststream is strongest.