Case Study

AI-Based Retail Theft Detection (Real-Time, Multi-Camera, Edge-Deployed)

A real retail deployment that goes beyond CCTV: multi-camera identity persistence + sequence-based behavioral AI to flag suspicious patterns before confirmed theft.

Computer VisionMulti-Camera TrackingEdge AIBehavior ModelingProduction Deployment

At a glance

CAMERAS / STORE

Synchronized streams

EDGE COMPUTE

Jetson

On-device inference

DETECTION

90%

Custom-trained YOLO

TRACKING

85%

DeepSORT + stitching

BEHAVIOR MODEL F1

~60%

Most challenging part of the system

REALTIME THROUGHPUT

~10 FPS

End-to-end multi-camera pipeline

Problem & outcome

The challenge

Traditional CCTV records incidents but doesn’t understand behavior. The goal was to detect suspicious behavior patterns in real time, across multiple camera views, using edge compute with minimal latency.

Real retail environment (bakery) with variable lighting and occlusions
6 synchronized cameras and cross-camera identity consistency
Behavior recognition with limited suspicious data and class imbalance

What we built

Multi-stage perception → tracking → behavior pipeline (not rules-only)
YOLO (detection + segmentation) for robustness under occlusion
DeepSORT + cross-camera identity stitching
3D CNN + MLP behavior classifier (sequence-based intelligence)
Edge deployment on Jetson with synchronized GStreamer streams

Architecture overview

The pipeline is designed to keep computation at the edge and reserve the cloud for small alert clips only.

End-to-end pipeline

6× synchronized camera streams (GStreamer)

YOLO v11s (custom) → detection + segmentation

DeepSORT tracking + cross-camera stitching

Pose/interaction features → 3D CNN + MLP classifier

Suspicious probability threshold → real-time alert

Cloud storage: short alert clips only (limited retention)

What makes it different

This is not just object detection or rule-based logic.

Sequence-based behavioral AI (temporal modeling)
Multi-camera identity persistence
ROI-free modeling (no hard-coded shelf zones)
Edge-first design for minimal latency

Deployment constraints

Real-time performance under limited edge compute and variable store conditions.

Edge-optimized inference pipeline
Stream synchronization via GStreamer
Robustness via segmentation masks
Measured thresholds for alert reliability

Performance summary

Performance (visual)

Detection accuracy90%

Tracking accuracy85%

Behavior F160%

False positives15%

Note: “False positives” is visualized inverted (lower is better).

Key metrics

Detection accuracy~90%

Tracking accuracy~85%

Behavior F1 score~60%

False positives~15%

Throughput~10 FPS

DeploymentEdge (Jetson)

Before vs after

BEFORE

CCTV records events but cannot interpret behavior
Manual review is slow and reactive
Single-camera systems fail in occlusion and multi-angle scenarios

AFTER

Behavior-based suspicion scoring (sequence modeling)
Cross-camera identity stitching for continuity
Edge-based real-time alerts with minimal latency

Hard problems solved

Behavior modeling complexity

Suspicious events are rare, varied, and hard to label — overfitting is a constant risk.

Structured validation pipeline
Iterative dataset refinement
Hybrid features (pose + scalar interaction signals)

Multi-camera tracking

Lighting variation, Re-ID ambiguity, and ID switching across angles.

Re-ID tuning + stitching rules
Synchronization via GStreamer
Measured false-positive tracking rate (~15%)

Business impact

Operational value

Early detection to reduce loss (not just recording)
Plug-and-play edge deployment for small/medium stores
Minimal cloud usage → lower cost + better privacy posture
Scales to multiple stores with consistent install pattern

Roadmap: multi-store identity fusion, graph-based behavior modeling, transformer temporal models, further edge optimizations, and LLM-based feedback learning to reduce retraining needs.

Building an edge AI system that must work in the real world?

If you need help designing reliable perception + tracking pipelines, we can help you ship an end-to-end system.