With the rapid development of society, video surveillance has progressively expanded into different areas of life, such as transportation, security inspection, banks. There are a large number of replaced and newly deployed cameras in fields such as safe cities, smart campuses and smart buildings, which leads to a huge amount of video data, slow retrieval speed in video examining, and low efficiency in understanding complete picture of videos. In this paper, we propose SurVizor, a visual analysis system to understand the key content of surveillance videos. We integrate multiple image features and employ time series analysis methods to explore key temporal patterns in the feature. We integrate multiple visualization views from three levels of video, feature, and frame to promote exploration, analysis and understanding of video content. We evaluate the proposed system through a case study based on real-world surveillance videos from multi-camera and a user study. The results demonstrate the usability and effectiveness of our system in analyzing and understanding the key content of surveillance videos.
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This work is partly supported by National Natural Science Foundation of China (62036009), National Natural Science Foundation of China (61972356), Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-A2020001).
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Sun, G., Li, T. & Liang, R. SurVizor: visualizing and understanding the key content of surveillance videos. J Vis (2021). https://doi.org/10.1007/s12650-021-00803-w
- Surveillance video
- Time series
- Visual analysis