Object Detection using Computer Vision
Logistics Organization
KEY IMPACT
Enabled near-real-time monitoring of people/vehicle events with structured analytics, and provided a reusable framework that combines CV detection, automation, cloud storage, and dashboarding — offering operational transparency and actionable metrics.
The Challenge
An organisation needed a scalable framework to monitor people and vehicles using a camera network. The goal was to detect objects (people/vehicles) reliably, blur faces for privacy, store the data in cloud storage, and produce downstream analytics (counts, percentiles, time-based detection stats) via a dashboard.
Our Solution
Designed and implemented a five-stage workflow covering object-detection model development, automation of the capture pipeline, cloud storage configuration, R&D tuning, and dashboard visualization. The camera network triggers image capture on motion; captured images are analysed through a CV model (detecting people & vehicles); faces are blurred; results are stored in cloud storage. The pipeline supports both serverless and hosted modes depending on user preference. Data cleaning/structuring then feeds into a dashboard layer where KPIs (e.g., number of detections by time slot, percentile of vehicle detections, correlation of detection event → time) are computed and visualised.
Real-Time Object Detection & Analytics System Architecture showing motion-triggered image capture, computer vision model for people and vehicle detection, cloud storage and data structuring, KPI computation, and dashboard visualization with AWS IoT Greengrass and Kibana
Results & Outcomes
Enabled near-real-time monitoring of people/vehicle events with structured analytics
Provided a reusable framework that combines CV detection, automation, cloud storage, and dashboarding
Offered operational transparency and actionable metrics
Technologies Used
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