Camera Control at the Edge with Language Models for Scene Understanding

2025-05-09

AI Summary

This work addresses the limitation of PTZ cameras—reliance on explicit programming and lack of natural language interaction capability—by proposing OPUS, a framework for edge-deployed, natural language–driven PTZ control based on a lightweight language model. Methodologically, OPUS introduces a novel “prompt optimization + knowledge distillation” paradigm for efficient on-device deployment: it eliminates visual tokens entirely, representing multi-view visual information solely through compact textual embeddings, thereby enabling cross-camera semantic fusion and context-aware environmental understanding. It provides a no-code, conversational interface for intuitive camera operation. Experiments demonstrate that OPUS achieves a 20% higher task accuracy than Gemini Pro and outperforms state-of-the-art prompt engineering approaches by 35%, while maintaining real-time inference on resource-constrained edge devices—approaching the performance of GPT-4.

Technology Category

• Computer Vision: Language and Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In this paper, we present Optimized Prompt-based Unified System (OPUS), a framework that utilizes a Large Language Model (LLM) to control Pan-Tilt-Zoom (PTZ) cameras, providing contextual understanding of natural environments. To achieve this goal, the OPUS system improves cost-effectiveness by generating keywords from a high-level camera control API and transferring knowledge from larger closed-source language models to smaller ones through Supervised Fine-Tuning (SFT) on synthetic data. This enables efficient edge deployment while maintaining performance comparable to larger models like GPT-4. OPUS enhances environmental awareness by converting data from multiple cameras into textual descriptions for language models, eliminating the need for specialized sensory tokens. In benchmark testing, our approach significantly outperformed both traditional language model techniques and more complex prompting methods, achieving a 35% improvement over advanced techniques and a 20% higher task accuracy compared to closed-source models like Gemini Pro. The system demonstrates OPUS's capability to simplify PTZ camera operations through an intuitive natural language interface. This approach eliminates the need for explicit programming and provides a conversational method for interacting with camera systems, representing a significant advancement in how users can control and utilize PTZ camera technology.

Problem

• Control PTZ cameras using LLM for scene understanding
• Improve cost-effectiveness via knowledge transfer to smaller models
• Simplify camera operations with natural language interface

Innovation

• LLM controls PTZ cameras via natural language
• SFT transfers knowledge to smaller edge models
• Converts multi-camera data to textual descriptions