Object Tracking

Deep Vision's data abstraction technology delivers real-time object tracking.

Deep Vision's novel concept of data abstraction quickly transforms abundant sensor data into a form that is easily classified and efficiently analysed.

The abstractions created from the raw data, coupled with their position on the sensor and relative positions over time, provide the foundation for real-time object tracking.

The abstractions are not dependent on the sensing platform, type of sensor or other objects within the sensor's data. Therefore, the abstractions, and by extension the objects they represent, can be tracked independent of sensor modality, sensor motion, and object motion.

With and without prior knowledge of the target objects, or their characteristics, Deep Vision's object tracking technology delivers a highly robust, real-time target locking solution.

Exploitation Value
  • Operator Assistance
  • Target locking
  • Motion Analysis and Prediction


  • Real-time object tracking
  • Simultaneous tracking of multiple objects
  • Operates in complex and cluttered environments
  • Context-based motion analysis
  • Sensor and object independent
  • Moving platform and moving target
  • Designation of a specific target whilst maintaining awareness of all others.
  • Target designation in one sensor type enables detection in disparate sensor types.


Evading Target

An evading human target being chased down in the woods. Real-time tracking in a cluttered natural environment with chaotic sensor motion. The target moves unpredictably through a wooded area, leaving and entering the field of view. The target is continuously reacquired as it re-enters, even when it is partially obscured and only visible for a fraction of a second. This video was recorded using legacy interlacing - very noisy.

Another evading human target being chased down in the woods but this time recorded using progressive scan. Note how extreme sensor motion, changes in perspective and scale and wildly unpredictable behaviour of the target, including obsfucation to the point of loss, has little affect, if any, on tracking capability.

Moving Target Moving Platform

In the maritime setting, a person in distress is robustly tracked and monitored, independent of the scale of the person, sea state, ocean and platform motion. In this instance it's a helicopter but it could just as easily be a UAV on a scouting mission.

The user selects the target to track, and tracking of that target will continue as long as the object remains within the field of view. Tracking is unaffected by target/platform motion, environmental effects (e.g. rain, sea spray), target scale, perspective, changing view or occlusion.

Designation of a fleeing car from the dash camera on a police vehicle. The car is tracked during the pursuit, demonstrating robust tracking in the scenario of moving target/moving platform.

Designation and tracking of a human target. The target is tracked whilst the camera experiences high degrees of motion. The tracking is independent of target/platform motion, scale/pose of the target, in addition to changing target/background composition and dynamic reacquisition.

Camouflaged and Slow Moving Targets

The operator designates a target at runtime and that target is tracked within the field of view, independent of target/platform motion, target scale/pose, and target/background composition. Note the extent of camouflage and track persistence through loss of focus.