SafeSight

Overview

SafeSight is an exploratory, work-in-progress project that explores real-time worker safety monitoring from video. It is being developed as a pipeline that reads a video stream, localizes people and equipment in the scene, follows them across frames, and evaluates what they are doing against a defined set of safety rules. Nothing here is deployed or validated. The work sits at the research and development stage, and the design is still being shaped.

Problem

Safety expectations on work sites are usually enforced by direct human observation. Where video is recorded at all, it tends to be reviewed manually and after the fact. The question SafeSight examines is what a video pipeline would have to get right to surface safety-relevant situations while they are happening. The relevant problem classes are well known: detections are noisy on a frame-by-frame basis, identities must stay consistent as people move behind obstacles and past each other, and raw scene state has to be distilled into a small number of events that an operator can actually read.

Solution

SafeSight is structured as a linear pipeline with one responsibility per stage. Video Processing takes frames from the stream and prepares them for analysis. Object Detection localizes the people and equipment present in each frame. Tracking links those localizations over time so that each subject keeps an identity across frames. Safety Rules evaluate the tracked scene state against a configurable rule set that can reference zones, proximity, and duration. Event Detection sits at the end of the pipeline and reduces rule violations into discrete, time-bounded events rather than per-frame flags.

Architecture

Video Stream
  ↓
Video Processing
  ↓
Object Detection
  ↓
Tracking
  ↓
Safety Rules
  ↓
Event Detection

Engineering Challenges

The design targets several problem classes at once. Detection output is unstable across adjacent frames, so the Tracking stage is built to hold identity through brief occlusions and crossings rather than treating every frame as a new scene. The Safety Rules stage is built to handle context that no single frame carries: an action is only unsafe relative to where it happens, what is nearby, and how long it lasts. Event Detection is built to collapse repeated rule hits into one event with a clear start and end, so a sustained condition does not flood the operator with identical alerts. Each stage is deliberately decoupled so it can be reasoned about and revised on its own.

Technology

Object Detection localizes subjects of interest per frame. Tracking maintains identity across frames and buffers short gaps. Video Processing handles frame preparation and stream pacing. Safety Rules encodes the conditions considered unsafe in an editable form, so rules can change without touching the vision stages. Event Detection turns sustained rule violations into discrete events with defined boundaries.

Current Status

SafeSight is an exploratory project and remains under active development. It is at the research and development stage: the pipeline stages are being designed and worked through individually, and integration of the full chain is still in progress. The rules vocabulary and the event boundaries are being refined as the stages are exercised. There are no measurements, field trials, or validation results to report yet, and the project is not deployed anywhere.