Zero-Shot Retail Theft Detection via Orchestrated Vision Models: A Model-Agnostic, Cost-Effective Alternative to Trained Single-Model Systems

arXiv cs.CV / 4/17/2026

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Key Points

Paza is a model-agnostic, zero-shot retail theft detection framework that detects concealment behaviors without training on proprietary datasets.
It uses a layered pipeline where low-cost object detection and pose estimation run continuously, and an expensive vision-language model (VLM) is called only when a multi-signal behavioral pre-filter is triggered.
The suspicion pre-filter (dwell time plus at least one behavioral signal) reduces VLM invocations by 240x, limiting calls to 10 per minute or fewer and allowing one GPU to cover 10–20 stores.
The system can swap VLM backends via OpenAI-compatible endpoints (e.g., Gemma 4, Qwen3.5-Omni, GPT-4o) without code changes, helping operators adapt as model offerings evolve.
On the DCSASS synthesized shoplifting dataset, the VLM component achieves 89.5% precision and 92.8% specificity at 59.3% recall in a zero-shot setting, with cost estimated at $50–100 per store per month and a privacy-preserving face obfuscation design.

Abstract

Retail theft costs the global economy over \$100 billion annually, yet existing AI-based detection systems require expensive custom model training on proprietary datasets and charge \$200-500/month per store. We present Paza, a zero-shot retail theft detection framework that achieves practical concealment detection without training any model. Our approach orchestrates multiple existing models in a layered pipeline - cheap object detection and pose estimation running continuously, with an expensive vision-language model (VLM) invoked only when behavioral pre-filters trigger. A multi-signal suspicion pre-filter (requiring dwell time plus at least one behavioral signal) reduces VLM invocations by 240x compared to per-frame analysis, bounding calls to <=10/minute and enabling a single GPU to serve 10-20 stores. The architecture is model-agnostic: the VLM component accepts any OpenAI-compatible endpoint, enabling operators to swap between models such as Gemma 4, Qwen3.5-Omni, GPT-4o, or future releases without code changes - ensuring the system improves as the VLM landscape evolves. We evaluate the VLM component on the DCSASS synthesized shoplifting dataset (169 clips, controlled environment), achieving 89.5% precision and 92.8% specificity at 59.3% recall zero-shot - where the recall gap is attributable to sparse frame sampling in offline evaluation rather than VLM reasoning failures, as precision and specificity are the operationally critical metrics determining false alarm rates. We present a detailed cost model showing viability at \$50-100/month per store (3-10x cheaper than commercial alternatives), and introduce a privacy-preserving design that obfuscates faces in the detection pipeline. The source code is available at https://github.com/xHaileab/Paza-AI.