Abstract
Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surveillance video applications, key frames are typically used to summarize important video content. It is very important and essential to extract key frames accurately and efficiently. A novel approach is proposed to extract key frames from traffic surveillance videos based on GPU (graphics processing units) to ensure high efficiency and accuracy. For the determination of key frames, motion is a more salient feature in presenting actions or events, especially in surveillance videos. The motion feature is extracted in GPU to reduce running time. It is also smoothed to reduce noise, and the frames with local maxima of motion information are selected as the final key frames. The experimental results show that this approach can extract key frames more accurately and efficiently compared with several other methods.
Highlights
With the development of various intelligent techniques, smart cities have attracted much attention in recent literature from both researchers and practitioners because of their great application values for facilitating social activities
A novel approach is proposed to extract key frames from traffic surveillance videos based on graphics processing units (GPU)
Motion information computation is implemented on an NVIDIA GeForce GTX 295 GPU and K20
Summary
With the development of various intelligent techniques, smart cities have attracted much attention in recent literature from both researchers and practitioners because of their great application values for facilitating social activities. According to the requirements of a smart city, city management system should provide many convenient and intelligent services to the public. Surveillance video service (SVS) is one of the most important services in a smart city. Valuable features, such as trajectories or visual appearances of moving objects, are captured and form the basis for intelligent traffic surveillance and public security. It is important to design intelligent video analysis techniques to efficiently mine this latent and valuable information. One of the most important strategies to adopt is leveraging effective machine learning techniques to analyze heterogeneous information involved in surveillance videos
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