Technology Overview

Smoking Detection is an AI-based video analytics technology designed to recognize human behavior associated with smoking directly from surveillance camera video.

Unlike conventional sensors that may detect smoke as a physical substance, AI-based Smoking Detection focuses primarily on the visual behavior of a person smoking. The technology analyzes people and their actions within the camera view to identify characteristic smoking gestures and behavioral patterns.

When the AI determines that observed behavior matches the criteria for smoking, it generates a detection event that can be used by the video management system for alerts, recording, event search, or other automated actions.

How Smoking Detection Works

The AI continuously processes video frames from a surveillance camera and identifies people within the monitored scene. Once a person is detected, the system analyzes relevant visual and behavioral information over a sequence of frames.

Rather than making a decision based on a single image, the AI can evaluate the person's posture, hand position, hand-to-face interaction, and repeated movements associated with smoking.

For example, smoking behavior may involve a combination of visual characteristics such as:

Repeated hand movement toward the mouth or face Characteristic hand and arm positioning Holding a small object near the mouth Repetitive smoking-related gestures over time Other visual patterns learned by the AI model

The combination of these observations allows the AI to distinguish potential smoking behavior from ordinary activities.

AI-Based Behavioral Analysis

Smoking Detection is fundamentally a behavior recognition technology.

A person simply holding an object is generally not sufficient to establish that smoking is occurring. Many everyday actions—such as drinking, eating, using a mobile phone, or touching the face—can produce visually similar poses.

For this reason, the AI model evaluates multiple visual cues and, where applicable, their progression over time. The resulting analysis produces a confidence level representing how closely the observed activity matches the smoking behavior learned by the model.

When the confidence level satisfies the configured detection criteria, a Smoking Detection event is generated.

Detection Zones

Administrators can define specific areas within the camera view where Smoking Detection should operate.

Detection zones allow the analytics system to focus processing and event generation on relevant locations while ignoring areas where smoking behavior does not need to be monitored.

For example, the same camera may contain both a monitored no-smoking area and another portion of the scene where detection is unnecessary. Configuring the appropriate detection zone helps ensure that generated events correspond to the intended monitoring area.

Confidence and Detection Parameters

AI detection is probabilistic rather than an absolute yes-or-no measurement. Each analyzed event may therefore be associated with a confidence value indicating how strongly the detected visual behavior corresponds to smoking.

Detection parameters can be configured to balance sensitivity and accuracy.

A more sensitive configuration may detect a wider range of possible smoking events but can also increase false alarms. A stricter configuration can reduce false detections but may miss smoking behavior that is partially obscured or visually ambiguous.

The appropriate configuration depends on the surveillance environment, camera position, operational requirements, and acceptable balance between missed events and false alarms.

Event Generation and Integration

Once smoking behavior satisfies the configured detection criteria, the analytics system generates an event.

The event can be integrated into the broader ACTi video surveillance workflow and may be used to:

Display an alert to an operator Highlight the corresponding camera Trigger video recording Create an event record for later search Generate an alarm or notification Initiate other configured system responses

This allows Smoking Detection to operate as part of a larger automated video surveillance and event management system rather than as an isolated analytic function.

Camera Placement and Image Requirements

Camera installation has a significant effect on detection performance.

Because Smoking Detection relies on observing relatively small human gestures, the person must occupy enough pixels in the video image for relevant body movements to be distinguishable.

For best performance, cameras should provide a clear view of the person's upper body, arms, hands, and head whenever possible.

Important installation considerations include:

Sufficient subject size within the image Clear visibility of the upper body and hands Appropriate camera viewing angle Adequate and consistent lighting Suitable image resolution and compression quality Minimal obstruction of the person Avoidance of excessive motion blur

Cameras installed too far from the monitored subjects may provide insufficient visual detail for reliable behavioral analysis.

Environmental Factors

Several environmental conditions can influence AI detection accuracy.

Poor lighting, strong backlighting, shadows, crowded scenes, partial occlusion, low-resolution video, excessive compression, and unusual camera angles may make smoking gestures more difficult for the AI to recognize.

Crowded environments can be particularly challenging when people's arms, hands, or faces are frequently blocked by other individuals.

Detection performance should therefore be evaluated under the actual operating conditions of the deployment.

Distinguishing Similar Activities

One of the primary technical challenges of Smoking Detection is distinguishing smoking from visually similar activities.

Examples can include:

Drinking from a cup or bottle Eating Holding a mobile phone near the face Touching or scratching the face Holding a pen or another small object Repeated hand gestures during conversation

The AI model uses learned visual and behavioral patterns to differentiate these activities. However, because some actions can appear highly similar from certain camera angles, occasional false detections or missed events remain possible.

Real-Time Processing

Smoking Detection is designed to analyze surveillance video continuously and generate events shortly after qualifying behavior is recognized.

The exact response time can depend on factors such as the AI processing platform, video frame rate, system load, network conditions, and the amount of behavioral evidence required before an event is confirmed.

Using multiple frames can provide more reliable behavioral recognition than evaluating an isolated image, while still allowing the system to provide timely notifications.

Privacy and Identity

Smoking Detection can recognize smoking behavior without requiring the system to determine the identity of the individual.

The analytic objective is to answer the question “Is smoking behavior occurring in this monitored area?” rather than “Who is this person?”

As a result, Smoking Detection can be deployed independently of facial recognition or employee identification technologies when individual identification is not required by the application.

Technical Benefits

AI-based Smoking Detection transforms existing surveillance video from a passive recording source into an active behavioral monitoring system.

Key technical advantages include:

Automated analysis of live surveillance video Recognition of smoking-related human behavior Continuous monitoring without constant operator observation Configurable detection areas and parameters Real-time event generation Integration with recording, alarm, and notification workflows Event-based video search and investigation Operation without requiring personal identification Performance Considerations

Smoking Detection is an AI-assisted analytics technology and should not be expected to identify every smoking event under all conditions.

Performance depends on the quality of the visual evidence available to the AI model. Small subjects, obstructed hands, poor illumination, extreme viewing angles, dense crowds, or brief and ambiguous gestures can reduce detection reliability.

System designers should therefore consider camera positioning, image quality, monitoring zones, detection parameters, and the characteristics of the actual environment when deploying the technology.

Testing and tuning under representative operating conditions is recommended to achieve an appropriate balance between detection sensitivity and false alarms.

Conclusion

AI-powered Smoking Detection enables surveillance systems to automatically recognize visual behavior associated with smoking and convert that observation into actionable security events.

By combining person detection, behavioral analysis, configurable monitoring zones, confidence-based decision making, and real-time event generation, the technology reduces dependence on continuous manual observation and allows operators to focus their attention on relevant events.

When deployed with suitable camera positioning and properly configured detection parameters, Smoking Detection can become an effective component of an intelligent video surveillance and automated event management solution.

There are several indoor and outdoor locations where the smoking is strictly forbidden. Some of the locations are not only the health hazard in terms of the impact from the smoke, but also a fire hazard, such as smoking in the gas station while refilling the car. The smoking in the gas station triggers an instant alarm. It is even possible to create an automatic voice alert at the gas station to remind the person to stop smoking immediately, which will be triggered by the AI-based smoking detection.

It is possible to use ACTi's Smoking Detection cameras together with ACTi's ZNR-series NVR or with Windows-based NVR 3 servers. The VMS will receive the alerts from the cameras and handle the events by video pop-up, sound alert, sending an email alert and more.

Furthermore, some of the latest ZNR-series NVR servers have the built-in Smoking Detection capability - it is possible to use a regular surveillance camera without built-in AI and still run the smoking detection thanks to the NVR-side AI capabilities. Read More