Why Do Home NVR Detections Cluster Near the Edges of Wide-Angle Frames?
Home NVR detections can cluster near wide-angle edges because lens projection changes object shape, pixel density, motion, and detector geometry across the frame.
A porch or room camera may show repeated people, pets, or false boxes near the left and right boundaries even when activity is distributed normally. Wide-angle optics map equal real-world angles into unequal image shapes, and edge objects often stretch, curve, or enter only partially. Detection, lens correction, motion gating, and tracking can amplify that spatial imbalance.
Wide-Angle Projection Makes the Frame Spatially Uneven
Fisheye and strong barrel-distortion projections preserve a large field of view by bending straight scene geometry. Near the boundary, an object can occupy an unusual shape and angular scale, while a conventional rectangular box includes more background than it would near the optical center.
Research on fisheye geometry adaptation finds that conventional detectors need geometry-aware adaptation for fisheye imagery and that ordinary boxes are a poor representation for distorted objects. Polygonal representations and camera-model information better match the edge geometry.
A detection heatmap counts image coordinates, not equal areas of the room. One strip near an edge may cover a doorway or a much larger angular region than a central strip, so an apparent image-space cluster can partly reflect scene mapping rather than detector bias.
Training and Rectification Change Edge Confidence
Many detectors are trained mostly on rectilinear images with upright objects. Stretched limbs, curved door frames, partial entries, and lower effective detail at wide-angle edges differ from that training distribution, moving confidence and localization errors in a position-dependent way.
A study of fisheye object detection evaluates detection methods for fisheye cameras and introduces distortion-aware methods for the domain. The work supports treating edge behavior as a projection and training problem rather than assuming one accuracy value applies across the whole frame.
Rectification can make objects more familiar to the model, but resampling stretches peripheral pixels and can blur detail. Cropping after correction also changes which boundary events remain visible, so corrected and uncorrected pipelines can produce different edge clusters from the same camera.
Motion Gates and Track Logic Can Multiply Edge Events
Objects often enter the scene through its boundaries. Motion detection triggers when new pixels cross the frame, and tracking begins with a partial object whose box changes rapidly as more of it appears; brief track loss can then create several detection events in the same edge region.
A recent pipeline for peripheral detection consistency reports particular difficulty near fisheye boundaries where radial distortion and nonuniform resolution degrade appearance. Its preprocessing and ensemble design improves consistency by explicitly targeting those peripheral conditions. This distinction remains visible during later household testing.
The failure boundary is interpreting every edge cluster as lens distortion. A door, reflective window, moving tree, timestamp overlay, privacy mask, or detection zone may genuinely concentrate triggers there. Normalize counts by observed activity and usable area before blaming the optical model.
Build a Position-Normalized Detection Heatmap
Divide the frame into center, middle, and edge bands, then label real objects, false positives, partial entries, and track restarts for daylight and night clips. Record raw and rectified coordinates, object pixel size, confidence, box shape, motion trigger, and track ID.
Compare edge detections against the behavior described in track reacquisition. Run the same clips with motion gating disabled, lens correction toggled, and equal-area scene regions so optical, scene, and tracker effects do not remain mixed.
Change the pipeline only when edge-conditioned precision or recall is worse after activity normalization. If the doorway truly occupies the edge, keep the coverage and tune its region; if distortion dominates, use geometry-aware preprocessing or training rather than a global confidence reduction.
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