HIMA: AI video analytics

To catch a violation, someone has to watch the screens constantly or scrub through the archive by hand. And even when an event is spotted, the information tends to get lost: it is unclear who should respond, what was done, and whether the issue was ever closed.

At Traffic Rock we built HIMA — an AI video analytics system that finds predefined events in video and audio streams and turns them into concrete tasks for the people responsible.

We needed a system that works not with a single predefined scenario, but across different sites and business processes.

At one site the priority is detecting a person in a restricted area. At another — flagging idle vehicles, an empty workstation, or an item carried past a control line. Somewhere speech has to be analysed; elsewhere an event must be matched against data from CRM, WMS or another internal system.

Recognition alone is not enough. If the AI finds a violation but the information stays inside the interface, the business process has not changed.

So we built HIMA around the full loop: camera → event → owner → response → outcome.

HIMA connects to video streams and analyses what is happening in near real time. The set of events is configured for the specific site and business need.

The system works with several types of data:

• detecting people, vehicles and objects
• registering crossings of predefined zones
• identifying inactivity or prolonged downtime
• recognising faces where the scenario allows it and it is legally permitted
• analysing speech and finding specific phrases or topics
• spotting breaches of regulations and safety rules
• linking events across multiple cameras
• matching what happens against the company’s internal systems

We deliberately avoided a fixed catalogue of a few ready-made detectors. HIMA’s architecture allows new events to be added for the processes of a specific business.

Say a truck enters the loading area, but work does not start on schedule.

HIMA registers the vehicle, tracks activity in the zone and creates an event once the idle time exceeds the set threshold. The card stores the camera, the timestamp and the video fragment. The responsible employee gets a notification, checks the situation and records the decision taken.

A manager sees a clear history rather than an endless camera feed:

• what happened
• where and when it happened
• how critical the event is
• who is responsible for responding
• how long the resolution took
• how the situation ended

Each event type can have its own priority, status, owner and acceptable response time. If a task is left unattended, the system can escalate the notification to the next level.

Recognising an object in prepared footage is only part of the job.

On a real site the lighting, distance, camera angle, stream quality and background noise all change. People occlude each other, cameras run at different frame rates, and the same action can look entirely different.

That is why every new scenario goes through several stages:

1. Define an event that genuinely affects money, safety or quality of work.
2. Check whether it is actually visible on the existing cameras.
3. Collect sample video and audio from the site.
4. Tune the models and recognition rules.
5. Review false positives and missed detections.
6. Wire up notifications and internal systems.
7. Agree acceptance criteria and measure the result.

This is how HIMA adapts to a real environment rather than to a polished demo reel.

HIMA brings several layers together:

• AI analysis. Models process video and audio, finding people, objects, speech and predefined events.
• Operational loop. Every event gets a card, a priority, a status and an owner.
• Notifications. The information reaches the employee who can verify the event and make a decision.
• Integrations. Where access is available, an event can be matched against CRM, WMS, 1C, access control or another corporate service.
• Analytics. Managers see recurring violations, response times, site load and the history of closed events.

As a result, the camera becomes a data source for running operations.

We also worked through HIMA use cases for the international market, including talks with prospective partners in Dubai.

For warehouses, construction sites, hotels and distributed facilities, centralised control, a shared response protocol and the ability to manage events across several locations at once matter most.

International presales helped us verify how well HIMA’s architecture fits different industries, requirements and operating models. That said, we do not present negotiations as a completed rollout: the scope of a pilot, the hardware and the success criteria are always defined separately.

Traffic Rock built a platform that can be adapted to different industries and site types.

HIMA can:

• work with video and audio streams
• recognise predefined events
• collect the evidence in a single card
• assign owners
• track responses and deadlines
• keep a history of decisions
• produce reporting per site
• connect to the company’s internal systems

The main result is not another recognition module. We assembled working logic that connects observation with a management action.

There is no universal list of events. The most valuable scenarios are the ones that regularly lead to losses, downtime, violations or complaints.

That is why a project does not start with selling cameras. First we go through the site’s processes, pick one or two meaningful events and test them against a real video stream.

If an event can be detected reliably and the business has a clear response procedure, HIMA turns it into a managed process.

Traffic Rock builds AI systems for real business needs — from video stream analysis to integration, notifying the right person and tracking the outcome.