# Plausible Object Motion

Plausible Object Motion is a sanity check that detects abrupt or physically impossible positional shifts of a cuboid centre between consecutive frames. For each object class, a maximum permitted distance threshold is configured - if the cuboid centre moves more than this distance from one frame to the next, a visual cue is shown and the violation is recorded in a per-frame violation list. This ensures that object trajectories remain smooth and realistic in accordance with the project guidelines.

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The check evaluates the Euclidean distance between the cuboid centre position in frame N and frame N+1. It does not assess acceleration or path curvature — only the frame-to-frame displacement magnitude.
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## Use Case

When annotating across large sequences, even experienced annotators can inadvertently misplace a cuboid in a single frame - causing an object to appear to teleport, then snap back. These single-frame errors are difficult to catch visually during fast annotation but produce large discontinuities that corrupt motion-dependent model features such as velocity estimation and trajectory prediction.

**Plausible Object Motion addresses this by:**

* Computing the frame-to-frame displacement of every tracked cuboid centre in real time
* Comparing each displacement against the class-appropriate maximum distance threshold
* Displaying a visual cue immediately when the threshold is exceeded
* Maintaining a violation list grouped by track ID so annotators and reviewers can audit all affected frames

**Common scenarios include:**

* A vehicle cuboid accidentally dragged to the wrong position in a single frame, producing an unrealistic jump
* Keyframe interpolation producing an intermediate frame position that overshoots the realistic path
* Copy-paste of a cuboid from a distant frame without position adjustment
* Tracking ID reassignment causing a cuboid to inherit an incompatible previous position

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## Benefits

**For Annotators**

* Instant Cue on Violation - The visual cue appears as soon as the displacement threshold is exceeded, enabling immediate correction before moving to the next frame.
* Class-Aware Thresholds - Each class has its own limit, so the same displacement that is acceptable for a fast vehicle is correctly flagged as impossible for a stationary pedestrian.
* Immediate, Non-Blocking Warnings - Alerts appear only when guidelines are violated, so the workflow is never interrupted unnecessarily.

**For Project Managers**

* Configurable Per Class - Thresholds can be set to match the exact speed and frame-rate assumptions in the project guidelines, with iMerit-recommended defaults available.
* Trajectory Integrity - Ensures training data used for motion prediction and velocity estimation is free from single-frame positional anomalies.
* Reduced Rework Cost - Positional errors caught at annotation time are significantly cheaper to fix than those identified during model training or post-delivery QA.

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## Reference Thresholds

The following values are iMerit-suggested starting points. All thresholds must be validated against the project's actual frame rate and sensor capture speed before go-live, and updated in the workflow configuration to match customer guidelines.

| Object Class | Max Distance (m) | Approx. Speed Equivalent | Notes                    |
| ------------ | ---------------- | ------------------------ | ------------------------ |
| Vehicle      | 5.0              | \~112 mph / 180 kph      | Default - configurable   |
| Pedestrian   | 1.5              | \~33 mph / 54 kph        | Default - configurable   |
| Cyclist      | 2.5              | \~56 mph / 90 kph        | Suggested starting value |
| Motorcycle   | 4.0              | \~90 mph / 144 kph       | Suggested starting value |

{% hint style="info" %}
These thresholds assume a typical LiDAR capture rate of approximately 10 Hz (one frame every 100 ms). If your dataset uses a different frame rate, recalculate the per-frame distance limit as: max\_speed (m/s) × frame\_interval (s).
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## Steps to Use

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### Configure Thresholds in the Project 

* Navigate to your project's recipe settings (category schema).
* Locate and enable the Sanity Checks section.
* For each object class, set the maximum permitted frame-to-frame displacement in metres.
* Consider your dataset's frame rate when setting values - a higher frame rate means smaller per-frame distances for the same real-world speed.
* Save and publish the recipe. Thresholds apply immediately to all active tasks on reload.
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### Annotate as Usual

* Open the task in the 3D Multi-Sensor Fusion Labeling Editor.
* Annotate and track objects using your standard cuboid guidelines.
* The system computes the cuboid centre displacement between consecutive frames in real time for every tracked object.
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### Respond to the Visual Cue

* When a displacement exceeds the class threshold, a visual cue appears on and adjacent to all the affected cuboids on the timeline.
* The cue identifies the frames where the violation was detected.
* Determine whether the cuboid was misplaced (correct the position) or whether the object genuinely moved that distance (adjust the threshold if warranted).
* The cue clears automatically once the displacement is within the permitted range.
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### Complete the Task

* Violations are non-blocking - you may submit the task with active rotation warnings.
* Unresolved violations remain visible to QA reviewers, providing context for improbably object motion.
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## Best Practices

* Calibrate to Frame Rate - Always derive thresholds from real-world speed limits divided by the frame rate, not from arbitrary round numbers. Document the calculation in the project guidelines.
* Set Conservative Initial Values - Begin with tighter thresholds and loosen them based on annotator feedback. It is easier to relax a threshold than to retroactively fix a large set of false negatives.
* Use the Violation List as a Review Tool - Encourage annotators to work through the violation list systematically at the end of each sequence rather than resolving cues one at a time mid-annotation.
* Differentiate by Traffic Context - Consider setting tighter thresholds for datasets captured in low-speed environments (car parks, urban stop-start) versus motorway sequences.
* Pair with Rotation Check - Motion and rotation violations often co-occur on misplaced frames. Reviewing both lists together accelerates correction.
* Flag Genuine High-Speed Events - If the dataset includes legitimately fast objects (emergency vehicles, racing scenarios), document an exception handling procedure so annotators know what to do.