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Author: Jeremy Huang

Limitations of Traditional Image Recognition Technology

In past surveillance camera applications, many environments had already been equipped with high-resolution cameras and image recognition systems. However, in practice, these systems often struggled with efficiency and lacked the ability to make informed judgments. Traditional image recognition technology primarily focuses on detecting and classifying individual objects—such as identifying whether a person, vehicle, or piece of equipment appears in the frame. However, these systems are typically unable to understand relationships between objects or grasp the full context of events.

For instance, in occupational safety management, a system may detect that a worker is not wearing a safety helmet, but it cannot determine whether the worker is entering a high-risk area or violating on-site safety protocols. Additionally, when users need to review abnormal behavior from a specific timeframe, they often have to rely on manual frame-by-frame video inspection, which is time-consuming and labor-intensive. This creates a disconnect between the actual needs of surveillance systems and the level of intelligence that current technologies can provide.

Vision Language Models: From Recognition to Comprehension

As artificial intelligence technology advances, Vision Language Models (VLMs) have emerged as a key enabler of the next generation of surveillance camera systems. VLMs are capable not only of recognizing objects in a scene but also of understanding behaviors and semantic relationships, then responding using natural language. Their core capability lies in the integration of deep learning across both visual and linguistic modalities, allowing the system to generate semantically rich descriptions of the scene—such as “a worker not wearing a reflective vest is approaching hazardous equipment” or “two vehicles collided at an intersection, and the white car may have run a red light.” This far surpasses the binary "yes/no" or "what is this?" outputs typical of traditional image recognition.

Moreover, VLMs support natural language interaction, enabling users to query the system directly through conversational questions and obtain immediate insights. Compared to conventional systems that require complex rule-setting and parameter adjustments, VLMs allow for much more intuitive human-computer interaction. When managers ask questions like “Were there any workers not wearing proper gear yesterday?” or “Did anyone remain in the restricted area after hours?”, the VLM can analyze video footage and provide accurate answers, significantly improving information retrieval efficiency while reducing manual workload.

Application Scenarios of VLM-Enabled Surveillance Systems

The true value of VLMs lies in transforming surveillance systems from basic data capture tools into intelligent analytical engines capable of semantic reasoning and event understanding. This technology has already shown tangible benefits across various industries. In manufacturing, VLMs can instantly determine whether workers are wearing personal protective equipment properly, detect high-risk incidents such as slips and falls, and assess potential impacts along with recommended responses.

In transportation and urban management, VLMs can generate narrative summaries of traffic incidents to support back-end personnel in assessing liability and determining responses. They can also identify unusual crowd gatherings or suspicious behavior in real time and trigger timely alerts.

In logistics, port operations, and energy sectors, VLMs prove highly practical as well. They can continuously monitor high-risk operational processes, detect unauthorized access, or provide immediate alerts when large vehicles and personnel are in close proximity. Additionally, because these models can automatically generate structured event summaries, users no longer need to manually review every scene to understand the big picture, significantly improving data management efficiency and decision-making speed.

Considerations for Enterprises and Government Agencies Adopting VLMs

Although VLMs hold promising potential, their practical implementation still faces a few challenges. Firstly, these models require high computational resources, which may lead to performance or cost constraints when deployed on edge devices. Secondly, because VLMs are trained on massive datasets, their ability to understand the nuances of specific local environments still requires localized tuning and ongoing refinement. Without this, there may be risks of misinterpretation or overgeneralization.

Furthermore, deployment must also consider integration with existing systems and compliance with cybersecurity policies and operational workflows to fully unlock the technology's value. Therefore, promoting the adoption of VLMs should not be limited to simply purchasing new tools—it requires a holistic approach involving system integration, data governance, and process reengineering. Collaborating with experienced partners who understand the industry context and starting with pilot projects in controlled environments is key to achieving long-term success in smart surveillance deployment.

Taiwan’s Supply Chain Drives the Upgrade of the Smart Security Industry

Taiwan possesses a complete information and communications technology (ICT) supply chain, combining strengths in chip design, camera module manufacturing, and software development. These advantages provide a solid foundation for the development and deployment of VLM applications. In recent years, local companies have actively invested in VLM-related R&D and applications, achieving meaningful results in areas such as occupational safety surveillance and traffic management.

These solutions go beyond traditional object detection by integrating advanced functions like event understanding, semantic feedback, and real-time alerts—empowering surveillance systems with real-time decision-making and risk management capabilities. Looking ahead, as smart city infrastructure continues to evolve, VLMs with semantic comprehension will play an increasingly vital role in smart security systems. They not only reduce labor costs but also inject innovative momentum into Taiwan’s security industry.

With ongoing improvements in technology and expanded international applications, Taiwan is well-positioned to secure a leading position in the global smart security sector.

📌 Disclaimer:

This content is provided for reference and informational purposes only. It aims to illustrate the application trends and technological development of Vision Language Models (VLMs) in the field of smart surveillance. The information has been compiled based on publicly available sources and general industry knowledge, and does not constitute promotion of any specific product or investment advice.

Reference

What are Vision-Language Models? NVIDIA Glossary

智慧⼯廠Vision AI - ⼯安成功案例分享

【AI 引領製造升級】VLM + 數位孿生：強化安控，提升生產效率

智慧監控新時代：NVR + AI 高效安全防護方案| Spingence Visionstar

即時識別與控制： VLM 在工廠的應用