The platform
VisionObject reads your live streams and uploaded footage, keeps a record of every person, vehicle and event of interest it finds, and gives operators, investigators and managers one shared view. Each task below lists what it does, beside the screen it is done in.
- 1Your cameras and footage
- 2Objects, events and timelines
- 3Operator, investigator, manager
- 4One sighting · followed upward
Observe: live and playback
Bring live streams and uploaded footage into one view.
Watch up to sixteen cameras with detections drawn on, with a plain status for each feed. Replay footage you upload as one timeline. VisionObject does not record live video: your recorder does, and the platform keeps a still for each sighting where a frame was captured.
- Live wall with detections drawn on, and a wall mode
- Each feed shown as live, behind, stalled or lost
- Uploaded footage replayed as one timeline, with gaps shown or skipped
- A still kept for each sighting, where a frame was captured
Understand: detection and description
Turn movement into sightings, described by what can be observed.
People, vehicles and carried bags are detected and followed within a camera. Each sighting is described by its type, its colours, short tags and, where it is legible, its plate. Zones, lines, times and counts turn sightings into events of interest.
- People, vehicles and carried bags detected and followed
- Colours and descriptive tags
- Plate text with a confidence value, where legible
- An AI scene reading of one still frame
Object type
Nine types are detected in each analysed frame and followed within one camera.
Only these nine types are followed. Anything else in view, such as an auto-rickshaw or a hand-cart, is named only by a scene reading.
- Cyclist: Bicycle
- Motorcycle carrying three people: Motorcycle
- White car: Car
- Blue van: Car
- Orange truck: Truck
- Green bus: Bus
- Person in red with a backpack, at the main gate: Person, Backpack
- Person in yellow with a handbag: Person, Handbag
- Person in uniform carrying a box: Person
- Person under an umbrella: Person
- Suitcase by the bench: Suitcase
- Person in red with a backpack, on the campus road: Person, Backpack
- Person beside the hand-cart: Person
People
- Person
Vehicles
- Car
- Motorcycle
- Bicycle
- Bus
- Truck
Carried items
- Backpack
- Handbag
- Suitcase
The whole list, in words
- Source
Search within: One camera; Every camera in your scope; Uploaded recordings; A department or zone, for events and figures
- Time range
Kept for each sighting: First seen; Last seen; The clearest frame, as a still, where one was captured
- Object type
People: Person
Vehicles: Car; Motorcycle; Bicycle; Bus; Truck
Carried items: Backpack; Handbag; Suitcase
- Colour
Recorded as: People: upper and lower clothing; Cars, motorcycles, buses and trucks: main colour
The twelve names: black, white, grey, red, orange, yellow, green, cyan, blue, purple, pink, brown
- Worn or carriedShown, not searchable
Tags: Backpack; Handbag; Hat; Face mask; Uniform; Umbrella; On a phone; Carrying a box; Rider with helmet
- Vehicle
Type: Car; Motorcycle; Bicycle; Bus; Truck
Body style: Sedan; SUV; Pickup truck; Delivery vanShown, not searchable
- Number plate
Kept for each read: Plate text as read; Confidence of the read; Whether it fits a valid format
Search: The whole plate, or part of it
Route: Near match: one character out, or a commonly confused character; The cameras where it was read, in time order
- Scene reading
Present: auto-rickshaw; two-wheeler; pillion-rider; car; truck; bus; bicycle; hand-cart; pedestrian; street-vendor; stray-animal
Scene: heavy-traffic; light-traffic; junction; footpath; divided-carriageway; flyover; street-lit; night-scene; daytime; low-visibility; heavy-rain; camera-obscured
Vehicle: vehicle-wrong-way; vehicle-parked-illegally; vehicle-stopped-in-junction; vehicle-overloaded; vehicle-damaged; vehicle-no-plate; vehicle-plate-obscured
Two-wheeler: motorcycle-no-helmet; motorcycle-triple-riding; motorcycle-on-footpath
Person: person-on-roadway; person-running; person-loitering; person-on-ground; person-climbing; person-carrying-large-object
Crowd: crowd-forming; crowd-blocking-road; crowd-dense
Hazard: collision; fire-or-smoke; weapon-visible; unattended-baggage; road-obstruction; spilled-load; flooding
- Movement
Events: Entered a zone; Item left still beyond a set time; At least a set number in view
Also: Left a zone; Crossed a line, with direction; Stayed in a zone beyond a set time
- Similar appearance
A group gives you: Every sighting in it, in time order; The cameras it appeared on; The stills to compare
Not recorded
- Speed
- Vehicle make and model
- Object size
- Distance between people
Not classified by any model
- Emotion or mood
- Intent, dangerousness or likelihood of offending
- Gender, ethnicity or other demographic classification
- Who a person is, from appearance alone
An AI scene reading is instructed to describe clothing, vehicles and behaviour only. Its text is not filtered.
Investigate: search and investigation
Search what was seen, review the candidates, check the source.
Filter sightings by type, colour, plate, camera and time. Ask for look-alike sightings across cameras, or see one plate's readable sightings as stops on a map. Every result is a candidate, with its still behind it where one was captured.
- Search by type, colour, plate, camera and time
- Look-alike sightings grouped as candidates across cameras
- One plate's sightings as a time-ordered route on a map
- Search by what a scene reading says
Respond: alerts and response
Work each alert with its evidence, where there is any, and record who assessed it.
Rules on live cameras raise alerts with a severity, each leading to the still of the sighting that raised it where there is one. Each alert stays New until a named person marks it. Alerts can be assigned, tagged and annotated; status and assignment can be set for a selection.
- Zone-entry and item-left rules, with schedules and repeat limits
- Zone-exit, line-crossing and dwell rules
- Alerts marked Acknowledged or False positive, assigned and annotated
- A rule muted automatically on a camera where it keeps being marked false
- Plate watchlist alerts
Three states, each recorded with a name and a time
- New
- Raised by a rule. Nobody has assessed it.
- Acknowledged
- A named person has taken it on as a real firing of the rule. It is not a finding about what happened, or about who was involved.
- False positive
- A named person judged the rule wrong here. It counts towards the false-alarm rate and towards automatic muting.
Marking a selection applies the same mark to each alert, under your name. It verifies nothing and closes nothing.
Not recorded. Reviewed, escalated and resolved are separate steps in a real response. They live in notes and in your own process, not as states in the product.
Operate: analytics
Measure how spaces are used and how well alerts perform.
Counts of people and vehicles by hour and area, plate-reading volumes, the alert funnel, the false-alarm rate by rule and camera health, with the headline figures beside the previous period. A count is of the people or vehicles a camera is following, and each of them is also kept as a sighting.
- Counts by object type, hour and area
- Thresholds on the number of people or vehicles in a camera's view
- A weekly brief for each organisation
Govern: access and governance
Decide who sees what, what runs and what is kept.
Everyone signs in with a role and, where needed, a list of cameras. Several organisations can share one deployment without seeing each other's alerts. Modules are switched per organisation, and sighting records are deleted after the period you set.
- Organisations, camera ownership and sharing
- Five roles and a camera scope for each person
- An audit log of changes and of plate, scene and route searches
- Retention periods set by an administrator
- Module switches for each organisation
Modules
Detection and tracking always run. Every other module is switched on or off for each organisation; switching one off stops it running and hides what it produced.
Detection and tracking
Always onPeople, vehicles and carried bags found in each analysed frame and followed within one camera.
Appearance
Optional, per organisationColours, short descriptive tags, and grouping of look-alike sightings across cameras.
Number plates
Optional, per organisationPlate text and a confidence value on vehicles large enough to read, plate search, routes and the plate watchlist.
AI scene reading
Optional, per organisationA short description and up to eight tags for a sighting. Sends one still of the whole camera view per sighting to the AI service you configure, with the object type, the plate as read, the camera's identifier, colours, how long the object was followed and the time; a failed request may go to a second configured service.
Weekly brief
Optional, per organisationA weekly fact sheet with a written summary. Sends aggregate figures, with the names of your rules, cameras, areas and watchlist labels, to the AI service you configure.
Walk through the platform with your operators.
We will take your team through live view, search, rules and the alert queue on demonstration footage, and say plainly where it stops.