Derived metrics in observability pipelines for Inflection signatures If we assume an immovable, straight-down (nadir) camera with no pitch, yaw, roll, or zoom, the geometry of the problem simplifies in a way that is almost ideal for defining observability metrics. The drone’s motion is now the primary source of variation across frames: translation along straight edges, and a change in translation direction at corners. That means we can design metrics that are explicitly sensitive to changes in planar motion and scene displacement while being largely invariant to viewpoint distortions. Those metrics can be computed per frame or per short window, aggregated over time, and then reintroduced into the observability pipeline as custom events that act as “inflection hints” for downstream agents. The starting point is to treat each frame as a node in a temporal sequence with associated observability features. With a nadir camera, the dominant effect of motion is a shift of the ground texture in the image plane. Along a straight edge, this shift is approximately constant in direction and magnitude (modulo speed variations), while at a corner, the direction of shift changes. We can capture this with a simple but powerful family of metrics based on inter-frame displacement. For each pair of consecutive frames, we compute a dense or block-based optical flow field and summarize it into a mean flow vector and a dispersion measure. The mean flow magnitude reflects how fast the ground is moving under the camera; the mean flow direction reflects the direction of travel. The dispersion (e.g., standard deviation of flow vectors) reflects local inconsistencies due to parallax, moving objects, or noise. Over straight edges, we expect the mean flow direction to be stable and the dispersion to be relatively low and slowly varying. At corners, the mean direction will rotate over a short sequence of frames, and dispersion may spike as the motion field transitions. This gives us three basic observability metrics per frame or per window: average flow magnitude, average flow direction, and flow dispersion. These can be logged as metrics in the observability pipeline and then aggregated over sliding windows to produce higher-level signals: direction stability (e.g., variance of direction over the last N frames), magnitude stability, and dispersion anomalies. Because the camera is fixed in orientation, we can also exploit frame differencing and spatial alignment more aggressively. For example, we can compute a global translational alignment between consecutive frames using phase correlation or template matching. The resulting translation vector is a robust proxy for the drone’s planar motion. Again, along straight edges, the translation vector’s direction is stable; at corners, it rotates. The