科研之友
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摘要
The drawback to original video of the blooming process is that it contains a large amount of data and redundant information. In order to provide researchers with a video for monitoring which is endowed with a high compression ratio, small amount of data, rich growth detailed information and natural fluency, a blooming video monitoring system based on a key frame extraction method was developed in this paper. System hardware included: one Personal Computer, Central Processing unit: Intel ® CPU T2300@1.66GHz, 1.24 G memory, one Microsoft high-definition cameras HD-3000?one shading carton box, and one DC LED lamp, etc., software development environment: WinXP Operating System, Microsoft Visual Studio 2008 Professional, OpenCV2.0. This system can be divided into five function modules: image acquisition module, core algorithm module, key frames judgment module, data storage examine module, and video composition preview module. The core of the system is the key frame retrieval method. This method is based on the flower growth characteristics. For example, the background for each shot is fixed in the blooming process, the color information distribution change little, and the relative movement for the different parts of flower in the blooming process is clear. In this paper, the author conducts research on a key frame retrieval method based on optical flow and entropy statistics for blooming video. Experiments showed that the sensitive details can be collected from the flower blooming process, then those key frames collected can be composed into video with detailed information about the flower blooming, and the video is smooth. When the system is in the practical application, first, we need to obtain the original video of the blooming process. Then according to the flower growth characteristics, we extract key frames using a method of optical flow and entropy statistics for the obtained original video. This system includes two different key frames judgment modes: number of key frames mode and orientation information entropy threshold mode. When a certain mode is chosen and the relevant parameter is set, then the key frame video composition can be completed with this system. Finally, it can compose a key frame video preview about the blooming process. In this paper, the lilies open process was taken as an example and three video capture experiments were conducted by choosing different lilies at different time. These achieved video monitoring based on the key frame retrieval method. Experiment results proved by the key frame extraction method showed that the blooming process video retained the flowers blooming details naturally because of a small amount of data (it could reduce to above 84.6% of the original video data in the experiments.). The system can provide the relevant personnel a time-saving (it is only about 15.4% below of the original video playback time in the experiments.) and convenient monitoring platform about the blooming process.
- 出版日期2014
