A sensitive object‐oriented approach to big surveillance data compression for social security applications in smart cities

Abstract

Surveillance has become a fairly common practice with the global boom in "smart cities". How to efficiently store and manage the vast quantities of surveillance data is a persistent challenge in terms of analyzing social security problems. Developing data compression technology under the analytic requirements of surveillance data is the key to solving the storage problem. Criminal investigation demands the quality preservation of sensitive objects, typically pedestrians, human faces, vehicles, and license plates; however, the analytical value of surveillance data is rapidly lost as the compression ratio increases. In this paper, we propose a sensitive object-oriented regions of interest-based coding strategy for preserving the analytical value of surveillance data. In the proposed method, instead of generating a saliency map based on human visual perception, we consider saliency as a set of characteristics important for object detection and recognition. By making this modification, almost all sensitive objects necessary in a criminal investigation are assigned high saliency value rather than only one or two salient regions. Motions in the temporal domain are integrated to place emphasis on moving objects, namely moving sensitive objects, which then gain the highest saliency. Finally, a saliency-based rate control algorithm embedded in High Efficiency Video Coding is used to maintain the quality of sensitive objects in the encoded video under a fixed bitrate. Experiments were conducted on two analytical indexes: Feature similarity and object detection accuracy. The results showed that by achieving the same feature similarity and object detection accuracy, our method can save 20% and 40% bitrate over High Efficiency Video Coding, respectively, for the storage of big surveillance data. be archived. Depending on the retention policy, video streams (which are in increasingly higher resolution) can consume storage rapidly, leading to high financial pressure to scale the storage infrastructure. To relieve the financial pressure, normally a high compression ratio is adopted to make a trade-off between the retention period and the video quality. However, according to previous research 1, the reduced quality of surveillance video severely degrades the performance in video analysis tasks such as object recognition and tracking. This makes critical applications such as large crowd management in public hotspot areas very unreliable 2.Our ability to maintain video analysis performance while resolving the surveillance video storage problem is dependent on our ability to accurately and effectively account for the characteristics of the videos. In normal applications, surveillance videos are used for analytical purposes. Taking a typical criminal investigation application, for instance, the analytically sensitive objects (e.g., pedestrians and vehicles) in the videos have much higher importance than the background. Therefore, it is reasonable to divide surveillance video frames into regions of interest (ROIs) and non-ROIs where R...

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