Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges

TL;DR

This paper introduces a new multimodal surveillance video dataset with detailed annotations, benchmarks state-of-the-art models on multiple tasks, and highlights the challenges and potential for improved AI understanding in surveillance scenarios.

Contribution

The paper presents the first annotated multimodal surveillance dataset, UCA, and benchmarks models for video-and-language understanding in surveillance, revealing new challenges and opportunities.

Findings

Mainstream models perform poorly on surveillance data

Multimodal learning improves anomaly detection

Constructed dataset enables future research in surveillance AI

Abstract

Surveillance videos are an essential component of daily life with various critical applications, particularly in public security. However, current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding, although they have obtained considerable performance. To address this issue, we propose a new research direction of surveillance video-and-language understanding, and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset, UCA (UCF-Crime Annotation), contains 23,542 sentences, with an average length of 20 words, and its annotated videos are as long as 110.7 hours. Furthermore, we benchmark SOTA models for four multimodal tasks on this newly created dataset, which serve as new baselines for surveillance video-and-language understanding. Through our experiments, we find that mainstream models used in previously publicly available datasets perform poorly on surveillance video, which demonstrates the new challenges in surveillance video-and-language understanding. To validate the effectiveness of our UCA, we conducted experiments on multimodal anomaly detection. The results demonstrate that our multimodal surveillance learning can improve the performance of conventional anomaly detection tasks. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI. The link to our dataset is provided at: https://xuange923.github.io/Surveillance-Video-Understanding.