Video Anomaly Detection
Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
Multimodal Large Language Models (MLLMs) represent a significant leap in AI's ability to interpret complex video streams, moving beyond simple recognition to joint visual-language reasoning. However, their practical reliability for high-stakes applications like Video Anomaly Detection (VAD) remains largely unexamined, particularly in 'in the wild' surveillance scenarios where errors carry substantial consequences.
This study reformulates VAD as a language-guided binary classification task, systematically evaluating state-of-the-art MLLMs on benchmarks like ShanghaiTech and CHAD. Our findings reveal a pronounced conservative bias in zero-shot settings, where MLLMs exhibit high precision but critically low recall, disproportionately favoring the 'normal' class. This 'decision gap' highlights the need for explicit class-specific instructions to shift the decision boundary, significantly improving F1-scores but underscoring recall as a persistent bottleneck.
Executive Impact
Our comprehensive analysis reveals that while MLLMs possess the foundational capabilities for video understanding, their direct application in real-world surveillance for anomaly detection faces significant challenges. The models' conservative bias and sensitivity to prompt specificity indicate a need for refined prompt engineering and calibration to achieve operational reliability. This has direct implications for security, resource allocation, and trust in AI systems.
0.64
Peak F1-score (ShanghaiTech)
Up to 5x
Recall Improvement Factor with Class-Aware Prompts
100%
Conservative Bias (Default Precision) in Zero-Shot
Deep Analysis & Enterprise Applications
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Model Performance & Bias
Prompt Engineering Impact
VAD Workflow Integration
Temporal Context Sensitivity
Challenge of High-Resolution Data
Initial zero-shot performance without specific guidance is extremely low, highlighting a strong conservative bias where models rarely flag anomalies, leading to high precision but near-zero recall. This reflects a default 'no-anomaly' stance unless explicitly instructed otherwise.
0.09
Baseline F1-score in Zero-Shot (ShanghaiTech)
The introduction of class-specific instructions dramatically improves recall, often by a factor of five or more. This suggests that MLLMs possess the underlying visual recognition capabilities but lack the 'categorical confidence' to act without explicit guidance. Prompt design is critical for shifting the decision boundary from conservative to practical.
| Prompt Type | Impact on Recall | Impact on F1-score |
|---|---|---|
| Generic (No Class-Aware) |
|
|
| Class-Aware (Specific Anomaly Labels) |
|
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Our proposed framework integrates MLLMs into a VAD pipeline by treating anomaly detection as a language-guided binary classification task. This ensures the model provides actionable decision boundaries rather than just ranking anomaly likelihood, crucial for real-time surveillance systems.
Enterprise Process Flow
Raw Video Input
→
Video Clipping (1s-3s Windows)
→
MLLM Inference with Prompt
→
Binary Anomaly Classification
→
Anomaly Notification
Longer temporal windows (2s-3s) generally improve MLLM reasoning, especially on lower-resolution datasets like ShanghaiTech. However, this effect is not universal; on higher-resolution datasets like CHAD, increased temporal context can sometimes introduce redundant information and obscure anomalies, leading to marginal or even negative F1-score deltas.
+0.17
Max F1-score Delta with Longer Clips (ShanghaiTech)
Higher visual fidelity does not automatically translate to better anomaly detection. MLLMs struggle with semantic interpretation in complex, high-resolution environments, suggesting that the bottleneck is not just about 'seeing' but 'understanding' context and intent, especially for subtle anomalies.
CHAD Dataset Performance
Despite the CHAD dataset featuring higher resolution and frame rates, MLLM performance peaked at an F1-score of only 0.48, significantly lower than the 0.64 achieved on ShanghaiTech. This indicates that higher visual fidelity alone does not resolve the semantic challenges of anomaly detection. The model's ability to interpret complex scenes and reason about events against ground truth remains limited even with richer visual input, underscoring the depth of the problem.
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