Computer Vision / Edge AI Engineer: Real-Time Shoplifting Detection for Pharmacies (Veesion.com-like system)

About the project We are a Montreal-based startup building a real-time, in-aisle shoplifting detection system for pharmacies, similar to Veesion.com. The system connects to a store's existing CCTV cameras, detects suspicious gestures (product concealment in clothing, bags or strollers), and sends a short video clip alert to staff on a mobile app within seconds. We're looking for an engineer, or a small team, to build the full pipeline: edge AI inference, an in-store hardware module, and a mobile alert app. Technical scope Video ingestion: live streams from existing NVRs and IP cameras (RTSP / ONVIF) Object detection: people, plus custom classes (product in hand, backpack, handbag, tote bag, stroller, store bag, jacket) Multi-object tracking: persistent IDs for each person across frames and cameras Pose estimation: skeleton keypoints to follow hand movements (hand to pocket, hand to bag, under clothing) Action / behavior recognition: classifying gesture sequences into theft categories False positive reduction: verification layer to confirm alerts before they reach staff Edge deployment: in-store hardware module running inference on up to 30 cameras per store, optimized for real-time performance, with remote updates and monitoring Data pipeline: labeling, data augmentation, and a retraining loop Mobile app: iOS and Android, real-time push notifications with video clips, feedback buttons Backend: API for alerts, stores, users, and clip storage Deliverables Phase 1: Paid test (1–2 weeks) Pull a live stream from an IP camera Detect and track people with persistent IDs Send a push notification with a 10-second clip to a phone when a defined event occurs Phase 2: MVP (8–12 weeks) End-to-end pipeline running on an in-store hardware module, handling up to 30 cameras per store Detection of 3 priority gestures: concealment under clothes, personal bag, backpack Zone configuration per camera (shelf, checkout, exit) Rules layer: severity tiers (high, medium, low), per-store toggles for each gesture, no duplicate alerts Alert clip generation (a few seconds before and after the event) Mobile app (iOS + Android): alerts feed, clip playback, "Theft / Not theft" feedback, "Declare an undetected gesture" button Camera health monitoring: alert when a camera goes offline, is obstructed, or when the NVR has issues Admin dashboard: stores, cameras, gestures, users Retraining pipeline: staff feedback becomes labeled data for new model versions Documentation and full source code handover Phase 3: Pharmacy-specific gestures Deblistering (opening packaging or removing security tags), shelf sweeping (bulk pickup into a bag), stroller concealment, store bag concealment.

Requirements

Proven experience deploying computer vision on live CCTV camera streams in production (please share a link or video) Experience running AI models on edge hardware at scale (many camera streams on one device) Experience with action recognition or pose-based behavior analysis Understanding of false positive reduction in real-world environments Privacy-by-design: no facial recognition, compliance with Quebec Law 25 / GDPR principles Good written English (French is a plus) Overlap of at least 3–4 hours with Eastern Time (Montreal) Engagement Contract, milestone-based payments. Possibility of a long-term role and equity for the right person. All code, models and data are owned by [Company name] (work-for-hire). Budget: [your range or "please propose"] To apply, answer these 3 questions (applications without answers will not be reviewed): Share a project where you processed live camera streams. What hardware, and how many cameras? How would you reduce false alarms when a customer puts their own phone in their pocket? What hardware and architecture would you propose to run detection on 30 cameras in one store, and why?