Enterprise AI Analysis
Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
Multimodal Large Language Models (MLLMs) demonstrate general competence in video understanding, but their reliability for real-world Video Anomaly Detection (VAD) remains largely unexplored. This study systematically evaluates state-of-the-art MLLMs on ShanghaiTech and CHAD benchmarks, reformulating VAD as a binary classification task. We find MLLMs exhibit a pronounced conservative bias in zero-shot settings, yielding high precision but low recall. Class-specific instructions significantly improve F1-score (e.g., from 0.09 to 0.64 on ShanghaiTech), yet recall remains a critical bottleneck. These results highlight a significant performance gap for MLLMs in noisy environments and provide a foundation for future work in recall-oriented prompting and model calibration for open-world surveillance, demanding complex video understanding and reasoning.
Key Takeaway: MLLMs show strong potential for video anomaly detection, but require explicit, class-specific prompting to overcome a conservative bias and achieve practical recall rates in real-world surveillance scenarios. Untamed, they prioritize precision over detecting critical events.
Executive Impact
Understanding the implications of MLLMs for real-world surveillance systems.
Problem
Traditional VAD systems struggle with real-world noise, semantic ambiguity, and the contextual nature of 'anomaly'. Current MLLM evaluations don't fully capture autonomous, real-time VAD challenges, often relying on curated datasets. The critical 'decision gap' for live systems—determining actionable alert thresholds—remains unaddressed. Errors in surveillance VAD carry significant consequences for public safety.
Approach
We developed a deployment-oriented VAD formulation for MLLMs, casting it as a prompt-guided binary classification task under weak temporal supervision. This approach directly targets the real-world decision boundary requirement. We systematically evaluated state-of-the-art MLLMs (Gemini) on ShanghaiTech and CHAD, analyzing how prompt specificity and temporal window lengths (1s-3s) influence precision-recall trade-offs in noisy surveillance contexts.
Result
MLLMs exhibit a pronounced conservative bias in zero-shot settings, resulting in high precision but a collapse in recall. Class-specific instructions are crucial, shifting the decision boundary and significantly improving F1-score (e.g., from 0.09 to 0.64 on ShanghaiTech). However, recall remains a critical bottleneck, demonstrating a significant performance gap for MLLMs in complex, noisy environments. Excessive prompt detail can also introduce semantic noise, with 'medium' prompts often outperforming 'long' ones.
Implication
While MLLMs possess general video understanding, they are not yet operationally reliable for autonomous real-world VAD without specific, recall-oriented prompt engineering. Future research must focus on explicit anomaly definitions, context alignment, and model calibration to overcome conservative biases and improve recall for open-world surveillance demanding complex reasoning.
0.64
Peak F1-Score (ShanghaiTech w/ class-specific prompts)
8.5x
Recall Improvement with Class-Specific Prompts
100%
Max Precision (Zero-Shot, indicating low recall)
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The Challenge of Zero-Shot Anomaly Detection
Multimodal LLMs in zero-shot settings exhibit a pronounced conservative bias, heavily favoring the 'normal' class. This leads to high precision but a critical recall collapse, limiting practical utility in real-world surveillance where missing an event carries high risk.
The models struggle with the high-stakes sensitivity required for security applications, demonstrating a lack of categorical confidence without explicit guidance. Performance degrades significantly in noisy and semantically ambiguous environments common to real surveillance footage, unlike cleaner curated datasets.
The Power of Prompt Engineering
Our research highlights the critical role of prompt specificity and class-specific instructions. By explicitly defining 'anomaly' in prompts, the decision boundary for MLLMs shifts significantly, leading to a dramatic recall improvement, often by a factor of five or more. This direct guidance unlocks the model's ability to identify abnormal events.
However, more detailed prompts ("long" prompts) do not consistently outperform "medium" ones; excessive verbosity can introduce semantic noise that distracts the reasoning engine. This underscores the need for strategically designed, recall-oriented prompting strategies rather than simply adding more detail.
Temporal Context and Realism
Evaluation across short clip windows (1s-3s) reveals that longer temporal contexts can generally assist MLLM reasoning, though this effect is not universally significant and varies by dataset. For lower-resolution data like ShanghaiTech, additional temporal context is more vital.
Crucially, simply increasing visual fidelity or resolution (e.g., in CHAD dataset) does not inherently solve the underlying challenges of video understanding in anomaly detection. Real surveillance footage's inherent noise and ambiguity require more than just clearer images; they demand sophisticated reasoning capabilities from MLLMs.
0.64
Peak F1-Score on ShanghaiTech (with class-specific prompts)
While MLLMs show promise, zero-shot Video Anomaly Detection in real-world surveillance still presents significant challenges. Achieving a peak F1-score of 0.64 on ShanghaiTech with optimal prompting highlights the need for tailored guidance.
Enterprise Process Flow
Video Stream
→
Video Clips
→
MLLM with Prompt
→
Classification Prediction
→
Anomaly Notification
| Prompt Type | Key Characteristic | Impact on Recall | F1-Score (Example SHT) |
|---|---|---|---|
| Generic Prompt | General instructions, no explicit anomaly definition | Low (Conservative Bias) | 0.09 (Human base, without class) |
| Class-Specific Prompt | Explicitly defines anomaly, focuses on desired behavior | High (Up to 8.5x increase) | 0.64 (GPT instant medium + class) |
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