Events

The first step in setting up Camlytics Service is choosing the appropriate detection profile. The profile defines what types of objects will be detected and how the counting is performed. Your choice directly impacts the accuracy and relevance of analytics results.

Recommended profiles

• AI vehicles & people – Use this when you need to detect both people and vehicles.
• AI people (overhead) – Best suited for overhead camera angles (top-down view) to count people.
• AI people (tilted) – Designed for cameras mounted at an angle, ideal for people counting.
• AI faces – Enables detection of gender and age through facial analysis.

Non-AI profiles

Overhead camera / Tilted camera (including high and low sensitivity options) – These are lightweight profiles that do not use neural networks. They are suitable for basic motion-based object counting, but they do not classify objects. For example, a person and a car will be treated the same. These profiles are less resource-intensive and ideal when you only need general object flow data, not detailed classification or advanced analytics.

If needed, you can also disable analytics completely for a channel by checking the “Channel events disabled” option. This can be useful if the camera is only used for streaming or recording, without analytics.

Additionally, you can adjust the detection threshold (human confidence level). Higher values reduce false positives but may result in missed detections.

We recommend testing several profiles with your camera setup to find the best balance of accuracy and performance.

Calibration is required only when using non-AI detection profiles. These profiles rely on traditional motion-based tracking, so accurate calibration is essential for reliable object detection, counting, and heatmaps.

To access calibration settings, go to Calibration in the channel menu.

In the Calibration section, you’ll see a ruler overlaid on a video snapshot. This ruler must be adjusted to match the real-world size of a typical object in your scene. Choose an average-sized object you want to track (usually a person), and scale the ruler to fit it accurately.

• For Overhead camera profiles, the ruler should match a person seen from above.
• If object sizes vary significantly in the scene, it's better to calibrate based on the smaller object.
• Proper calibration ensures that the green tracking boxes closely match the size of actual objects.

Examples of good calibration

• Green tracking boxes align with real object dimensions.
• Tracking is smooth and consistent.

Examples of bad calibration

• Ruler set too large or too small.
• Tracking boxes are oversized or tiny, leading to poor results and unstable analytics.

For Tilted camera profiles, it’s also critical to set the marker correctly, as it affects the system’s understanding of minimum and maximum object sizes for detection.

Once calibration is complete, you can proceed to define the Area of Interest, detection lines, and zones.

Triggers are core elements that define what types of movement and activity the system should detect and respond to. They let you track object flow, count events, measure speed, and generate automated responses.

There are three types of triggers:

• Lines
• Zones
• SpeedLines

Lines

Lines are used to count objects crossing a virtual line in the video. You can configure:

• One-way or two-way detection
• Entry/exit logic
• Object type filters (e.g., only people or vehicles in AI profiles)

Lines are ideal for entrances, gates, hallways, or any directional flow monitoring.

You can also enable Tailgating detection by checking the Tailgating option for a line. This event triggers when two objects cross the same line within a short interval (typically under 1 second).

Tailgating detection is especially useful for access control scenarios, such as monitoring office entry points to detect when someone follows another person through a secured gate or door without proper authorization. It works best with overhead cameras and people-counting profiles.

Zones

Zones detect objects entering, moving, or dwelling in specific areas of the video frame.

Each zone has two built-in events — Zone joined and Motion started — which are always active and do not require configuration.

In addition, zones support two optional and configurable event types:

• Object dwell – triggers when an object stays inside the zone longer than a specified dwell time (in seconds)
• Crowd appear – triggers when a defined minimum number of objects are present in the zone for a set duration

These advanced triggers are useful for detecting loitering, queues, overcrowding, or unusual idle behavior within critical areas.

SpeedLines

SpeedLines allow for both speed measurement and trajectory-based counting.

• Measure how fast objects move between two lines
• Set thresholds to detect overly fast or slow movement
• Ideal for traffic monitoring, speeding alerts, or identifying abnormal pedestrian behavior

In addition to speed analytics, SpeedLines can also be used to count objects moving along specific paths.

For example:

• At an intersection, you can count vehicles turning right separately from those going straight across a perpendicular road
• Useful in traffic analysis, flow segmentation, or behavior mapping in complex environments

SpeedLines are powerful when you need to understand how fast and in which direction people or vehicles are moving.

All triggers can be layered and combined within a single scene. You can name them, export event data, connect them to APIs or webhooks, and use them for real-time alerts or historical analysis.