Computer Vision

Depth-Based Spatial Analysis Between Detected Objects

A Python vision component combines object detection, depth estimation and spatial-analysis logic for scene inspection.

Original depth-estimation demo showing object detections and estimated distances; displayed values are demo output, not an accuracy benchmark
Original depth-estimation demo showing object detections and estimated distances; displayed values are demo output, not an accuracy benchmark

The problem

The task was to estimate spatial relationships between objects in a scene. Object boxes alone do not describe depth, so the processing path needed both detection and a depth estimate that could support distance and navigation-zone logic.

What I built

  • A Python project integrating a Depth Anything V2 depth-estimation component.

  • An object-detection component and utilities for spatial calculations.

  • Navigation-zone logic alongside object-distance estimation.

  • Configuration and a reproducible environment for running the project.

The delivered source separates detector, depth estimator and navigation logic so those components can be inspected or replaced independently.

The outcome

The handoff provides a starting point for scene-level spatial analysis. Monocular depth and estimated distances require validation against the intended camera and environment; this case study does not claim calibrated measurement accuracy or safety-critical navigation readiness. The value demonstrated is the integrated processing component, with deployment validation remaining application-specific.

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