Abstract
Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.
Highlights
IntroductionVideo surveillance systems are widely distributed in urban streets and roads, commercial places, residential areas, bank outlets, stations, terminals, airports, and other public places, playing an increasingly important role in public security
Video surveillance systems are widely distributed in urban streets and roads, commercial places, residential areas, bank outlets, stations, terminals, airports, and other public places, playing an increasingly important role in public security.rough the video surveillance system, suspicious signs and objects can be found in time and monitored closely, to avoid the occurrence of criminal harm effectively. e police can obtain information about criminals by surveillance videos and inquire about the locations of suspected vehicles and personnel
By fusing the object detection algorithm based on deep learning and the super-resolution algorithm, we can track the object of the surveillance video in real time, pick out the keyframes that change significantly, and perform super-resolution reconstruction of the key object. is provides the police and judges with clear and high-resolution objects, which can assist in the investigation and review of related cases. e method proposed in this paper can solve the problems of traditional video surveillance, provide effective assistance to surveillance video viewers, and effectively serve the public security field
Summary
Video surveillance systems are widely distributed in urban streets and roads, commercial places, residential areas, bank outlets, stations, terminals, airports, and other public places, playing an increasingly important role in public security. We proposed a super-resolution method based on a deep fusion network for surveillance video object detection. We designed a comprehensive surveillance video analysis framework that integrates object detection algorithms, keyframe selection algorithms, and super-resolution algorithms. It solves the problem of large workload, easy object loss, and low resolution in public security video data analysis. E main contributions of this paper are summarized as follows: (1) We designed a novel comprehensive analysis framework for surveillance video It improves the efficiency and accuracy of video analysis by combining object detection, keyframe selection, and super-resolution algorithms.
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