Traffic Video Analysis / Automatic Video Incident Detection - XAID™

X-AID™ Traffic Video Analysis - Automatic Video Incident Detection (AVID) is different from others because it implements Artificial Intelligence and Machine Learning algorithms to achieve 75% less false alarms ratio and 100% better detection accuracy than traditional AID systems.

See demonstration video.

CCTV systems are the most widely used traffic monitoring systems in the world. And yet, the analysis of hundreds of simultaneous streams is still being done by operators. The systems for video analytics that have been used in the past are considered as not reliable due to their high false alarm rate and rather low detection accuracy.

X-AID™ uses deep learning technologies to perform incident detection and classification in real traffic environment. Verified in comparative tests, in realistic conditions (all weather conditions, bad illumination, poor video streams ...), X-AID™ outperforms traditional systems in all important performance parameters (up to 3x better detection, up to 4x lower false alarms rate, and up to 2x better counting and classification accuracy). That way, it finally meets needs and expectations of Traffic Agencies on systems for video analytics.

  • Area of Use:
    • Road/Highway/Bridge AVID and Traffic Data
    • Tunnel AVID and Traffic Data
    • Traffic Irregularities Detection
    • Traffic Flow Monitoring
  • Detections:
    • Wrong-way Driving
    • Stopped Vehicle
    • Traffic Slowdown/Congestion
    • Slow vehicle
    • Pedestrians
    • Reduced or Loss of Visibility (smoke, fog, etc.)
    • Debris on the Road
    • Hard shoulder driving
    • Truck /Slow Vehicle in High Speed Lane
    • Trip wire detection
  • Not affected by common problems:
    • Camera Vibrations
    • Weather Conditions
    • Variations in Illumination
    • Low Video Quality
  • Traffic Video Analytics:
    • Average Vehicle Speed
    • Traffic Volume
    • Vehicle Classification
  • Advanced Functionalities:
    • Digital Video Management System and Digital Video Recorder
    • System Configuration Tool – calibration, detection zones, alarm thresholds and configuration

X-AID™ Videos

XAIDTM - Areas of Use


Some of the examples on how X-AID™ handles situations that are problematic for traditional video detection:

Direct light influence



Luminance variations


Detection of camera shift


Recognition of small objects and complex scenes


Pedestrians detected between vehicles


Problems of traditional AVID - solved

Working in direct light on the exit of the tunnel

Working in direct light on the exit of the tunnel

Automatic Video Incident Detection is the fastest way to detect a traffic incident and, as such, exponentially increases awareness about traffic conditions, and traffic safety.

Video detection systems require ideal working conditions (camera placement, constant illumination, environmental impacts, moving traffic), to reach ideal performance. In real life operation, ideal working conditions are not met. Traditional AVID systems are typically far from performance rates stated in their brochures (detection rate is typically <50% and false alarm rate is typically >5/camera/day).

The main advantage of XAID system, comparing to traditional systems, is ability of learning (fine tuning) which is based on Artificial Intelligence and Machine Learning algorithms. Learning ability of XAID enables it to learn how to perform in real-life conditions and tune its performance as close as possible to theoretical. Traditional systems do not have learning capability.

Technical specifications of video analytics systems describe operation and performance of the system in theoretical (ideal) conditions. In real applications, cameras are usually not ideally positioned, illumination is not ideal and environmental influences are expected (shadows, obstacles, etc).

Because of these reasons, ideal performance of the system most likely cannot not be reached. Yet, X-AID™ will reach much better performance than traditional systems, due to its fine tuning ability.

Connection between AVID and safety management

With hundreds of cameras deployed throughout highways and tunnels delivering the immense amount of information simultaneously, it is impossible, even for a group of human operators, to monitor and evaluate the information effectively or efficiently. Reliable Video Automatic Incident Detection system is a must in such environments because it significantly improves the efficiency of traffic management systems. It automatically reports dangerous situations and irregular traffic conditions, such as stopped vehicle, slow vehicle, driving in opposite direction, pedestrians in traffic lanes, dropped cargo, fire/smoke in a tunnel, or traffic congestion.

Main constrains in Video Automatic Incident Detection industry is never ending trade-off between the need to have the least possible number of false alarms and still not to miss some of alarms that did occur on the road.

Today’s traditional AID systems have come to dead end with technology currently being used.

X-AID™, the new generation of AID has done a big step further and solves critical issues traditional AID systems can’t solve - unique and the most advanced algorithms based on cognitive recognition open path to the new reliability standard in video detection industry. Machine learning setup process enables fine tuning for the best fit to the particular scene and environmental condition, and experience-based improvement in reliability and accuracy.

Case Studies

Click to open documents:

M4 Russia

Fløyfjell Tunnel

Amir Kabir Tunnel