Computer Vision Built for Crowded Places
Digeiz models detect, track and classify visitors across hundreds of cameras — even in dense crowds, occlusions and changing light.
Transform visual input into structured behavioral analytics.
Detection and segmentation
Deep neural networks identify human silhouettes and extract spatial coordinates and visual features used for trajectory analysis.
Detections · Spatial coordinates · Background segmentationTracking trajectories
Tracking algorithms connect detections over time to build coherent trajectories through motion prediction, appearance similarity and spatial consistency.
Motion prediction · Appearance similarity · Spatial consistencyPrivacy-preserving re-identification
Feature vectors use visible clothing appearance cues such as colors and shapes, excluding facial features. The system cannot identify or authenticate individuals.
Clothing appearance · No facial recognition · No biometric identificationCounting and flow analytics
Calibrated zones classify trajectory events such as entry, exit or pass-by to measure traffic at key points of interest.
Entrances · Stores · Corridors · Screens · Event areasFrom trajectories to business insights
Aggregated trajectories generate visitor journeys, dwell times, cross-visits and retail media screen exposure.
Journeys · Dwell time · Cross-visits · Media exposure