Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision
This is an incremental improvement for CCTV safety monitoring, offering a more practical and efficient anomaly detection solution.
This work tackles the problem of clip-level anomaly detection in CCTV surveillance under weak supervision by evaluating compact vision-language models (VLMs) with parameter-efficient adaptation, achieving performance on par with or exceeding established approaches while maintaining competitive per-clip latency.
CCTV safety monitoring demands anomaly detectors combine reliable clip-level accuracy with predictable per-clip latency despite weak supervision. This work investigates compact vision-language models (VLMs) as practical detectors for this regime. A unified evaluation protocol standardizes preprocessing, prompting, dataset splits, metrics, and runtime settings to compare parameter-efficiently adapted compact VLMs against training-free VLM pipelines and weakly supervised baselines. Evaluation spans accuracy, precision, recall, F1, ROC-AUC, and average per-clip latency to jointly quantify detection quality and efficiency. With parameter-efficient adaptation, compact VLMs achieve performance on par with, and in several cases exceeding, established approaches while retaining competitive per-clip latency. Adaptation further reduces prompt sensitivity, producing more consistent behavior across prompt regimes under the shared protocol. These results show that parameter-efficient fine-tuning enables compact VLMs to serve as dependable clip-level anomaly detectors, yielding a favorable accuracy-efficiency trade-off within a transparent and consistent experimental setup.