AI video analytics for parking facilities
For parking operators, mall and building managers and facility teams who already have cameras on entries, ramps and bays. Eye AI runs on an on-site computer, reads those cameras and counts vehicles in and out by lane, times how long each vehicle stays, and labels visits as parked or passing. Each event keeps a snapshot and time, which helps when a customer disputes a stay.
What goes wrong on sites like yours
Entry counts come from barriers or tickets that miss tailgaters and walk-ins.
Long stays and abandoned vehicles go unnoticed.
Disputes about arrival time have no evidence.
Busy ramps and levels have no record of when traffic peaks.
A camera fails and the gap is found only when someone needs the footage.
What Eye AI does here
| Analytic | What it does on your site | Status |
|---|---|---|
| Line crossing and counting | Counts vehicles in and out by lane or level, with direction | Live |
| Parking and dwell time | Times each vehicle in a bay or zone; parked or passing; visit durations | Live |
| Vehicle zone control | Logs vehicles entering zones where they should not be | Live |
| Nothing-is-silent safety net | Shows movement the AI could not name | Live |
| Stream health | Records whether each camera was working | Live |
| Event history and reports | Searchable history, day-by-hour grid, CSV export | Live |
| Vehicle and person details | Estimates vehicle type and colour | Beta |
| Number plates (ANPR) | Reads plates at entry lanes where the angle suits | Beta |
| Loitering detection | Logs people staying in an area beyond your threshold | Beta |
A day on your kind of site
Imagine a two-level car park with one entry lane, one exit lane and a row of bays on each level. The manager sets a counting line on each lane and a dwell zone over each row of bays. By midday the history shows entries and exits by hour and the busiest period. One vehicle stays in a bay far beyond the usual visit and is flagged as a long stay with a start time and snapshot. A customer later claims arrival was earlier; the manager checks the entry snapshots for that hour. At day end the grid is exported to CSV. If plate reading is wanted, the entry lane is surveyed first.
Reports and evidence you get
- Vehicle counts in and out by lane and level, by hour
- Visit start, end and duration, parked or passing
- Day-by-hour grid and CSV export
- Plate log with snapshot at surveyed lanes (Beta)
- Camera uptime history
Why existing cameras matter here
Car parks are already covered by cameras on lanes and ramps. Eye AI reuses them through an on-site computer, so there is no sensor in every bay and no new cabling.
Honest limits for this industry
- Counts need one camera setup per level or lane; a single view cannot cover a whole structure.
- Low light, headlights and basement glare reduce accuracy; dark vehicles at night can be missed.
- Occupancy and capacity charts are not a finished report; the underlying dwell and counts exist.
- Reserved-bay or authorised-vehicle checks need plate reading, which is Beta and needs a site survey.
- A visit can split if the system restarts mid-visit.
Questions
Can it count free bays?
Not as a finished occupancy report today. It gives counts and dwell.
Can it read plates?
Yes, in Beta, after a site survey of the lane.
Can it flag a car in a reserved bay?
Not without plate reading and a bay list; that is not offered today.
Does it need sensors?
No. It uses your existing cameras.
Which of this looks like your site?
We read every request and reply within 24–48 hours (working days, Monday to Friday) with questions and a proposed solution.
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