Operational Risk Factors in Computer Vision
Operational risk factors in computer vision refer to real-world conditions and system-level variables that can degrade the performance of vision algorithms once deployed. These risks can stem from environmental conditions, sensor limitations, data mismatches, or system integration challenges.
The following table provides a summary of risk factors. Where applicable, a T&E guide providing a detailed example is listed as well as functionality from NRTK that can be used to simulate the operational risk.
Some of these perturbation functions are not implemented in NRTK directly but can be simulated using the generic Albumentations perturber which provides a wrapper around functionality of the Albumentations library. Some of the risk factors listed don’t have any associated functionality or documentation in NRTK but may be covered in the future.
Risk Factor |
Related Function in NRTK |
T&E Guide |
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High-Frequency Vibration
Vibrations, such as wind, in the sensor platform induce jitter and blurring.
Impact |
Reduced effective resolution, frame-to-frame tracking performance. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org
Target Out of Focus
Target is out of focus (due to sensor optics settings, rather than atmospheric / environmental issues.)
Impact |
Model may underperform in proportion to loss of resolution w.r.t. training data. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org
Sensor Noise
The sensor data exhibits noise as a result of poor lighting, high ISO settings or overheating.
Impact |
Model may underperform in proportion to density of noise in image data. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
No sample available.
Dirt / Specularities on Lens
Obscurations on lens cover; IR may cause reflections or highlight imperfections in PTZ dome.
Impact |
Obscured or out-of-focus image regions; specularities may confuse algorithms. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org
Water Droplets on Lens
Droplets on the lens caused by rain or other factors can obscure or blur parts of the image.
Impact |
Obscured or out-of-focus image regions; specularities may confuse algorithms. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org
Extreme (Low / High) Illumination
Lighting conditions and camera settings result in excessive or insufficient illumination.
Impact |
Image has low contrast or dynamic range, reducing usefulness. |
Root Cause |
Target |
Affected Domains |
All |
Look Angle Different from Training Data
Operational viewpoint differs from those in training data.
Impact |
Model performance degrades due to lack of viewpoint coverage. |
Root Cause |
Inferencing |
Affected Domains |
UAV, WAMI, Satellite |
No sample available.
Shadows
Strong shadows are cast in the target area due to direct illumination.
Impact |
Features of interest in shadows may be undetectable. |
Root Cause |
Target |
Affected Domains |
All |
mevadata.org
Mist / Fog / Snow / Etc
Weather conditions reduce visibility between sensor and target.
Impact |
Targets become occluded or have lower contrast. |
Root Cause |
Optic Path |
Affected Domains |
Ground, Sea |
mevadata.org
Clouds
Clouds obscure targets, and may be transient or unpredictable.
Impact |
Targets not visible or have reduced contrast. |
Root Cause |
Optic Path |
Affected Domains |
UAV, WAMI, Satellite |
viratdata.org
Metadata Incorrect
Metadata stream is out of sync or contains incorrect values.
Impact |
Algorithms may use incorrect models or misinterpret data. |
Root Cause |
Labeling / Operating input |
Affected Domains |
All |
No sample available.
Burned-in Metadata
Metadata is overlaid directly on pixels instead of provided separately.
Impact |
Obscures target pixels and confuses detection or stabilization algorithms. |
Root Cause |
Sensor |
Affected Domains |
All |
Example UAV frame from FFMPEG project
Video Codec Artifacts
Compression errors from overloaded camera processors or poor settings.
Impact |
Visual glitches such as smearing or pixel corruption. |
Root Cause |
Inter-frame |
Affected Domains |
Ground, Sea, UAV |
mevadata.org
Video Feed Failures
Hardware or transmission issues interrupt video feed.
Impact |
Causes disruption of object tracking or pipeline shutdown. |
Root Cause |
Inter-frame |
Affected Domains |
Ground, Sea, UAV |
Camera overheating, doers-brc@kitware.com
Unstable Frame Rates
Feed is encoded at inconsistent rates, often due to overload.
Impact |
May drop or duplicate frames, confusing motion-based algorithms. |
Root Cause |
Inter-frame |
Affected Domains |
Ground, Sea, UAV |
mevadata.org
Glint / Glare
Bright reflections due to lighting, target materials, or angles.
Impact |
Can obscure targets and skew autoexposure or detection. |
Root Cause |
Target |
Affected Domains |
All |
“A data set for airborne maritime surveillance environments”, Ribeiro et al., IEEE Trans. Circuits & Systems for Video Technology, 2017
Night Mode / Low-Light Behavior
In low light, camera may switch to monochrome or different capture mode.
Impact |
Color data lost; resolution may be reduced slightly. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org
mevadata.org
Atmospheric Turbulence
Localized distortion due to atmospheric conditions.
Impact |
Object detection or tracking may degrade. |
Root Cause |
Optic Path |
Affected Domains |
Ground, Sea, UAV |
Shot Boundary
Sudden camera motion creates a new view, invalidating prior context.
Impact |
Detectors and trackers need to restart. |
Root Cause |
Inter-frame |
Affected Domains |
Ground, Sea |
mevadata.org
Radial Distortion / Fisheye Artifacts
Wide-angle lenses cause distortion at the image periphery.
Impact |
Alters appearance and trajectory of objects. |
Root Cause |
Sensor |
Affected Domains |
Ground, Sea |
mevadata.org