A key frame extraction method for processing greenhouse vegetables production monitoring video

• Published in: Computers and electronics in agriculture Vol. 111; pp. 92 - 102
• Main Authors: Ma, Juncheng, Li, Xinxing, Wen, Haojie, Fu, Zetian, Zhang, Lingxian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2015
• Subjects: Clustering; Diseases; Extraction; Frames; Greenhouse vegetables; Greenhouses; Key frames; Mean pixels value; Monitoring; Monitoring video; On line clustering; Online; Similarity; Vegetables; Visual saliency; On line clustering; Greenhouse vegetables; Monitoring video; Mean pixels value; Key frames; Visual saliency
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2014.12.007

•A new method for extracting key frames of greenhouse videos was proposed.•The method took full considerations of characteristics of greenhouse videos.•The method combined the visual saliency and online clustering.•The IG algorithm was modified by two parameters confirmed by a new measurement. Research reported in this paper aims to improve the identification of greenhouse vegetable diseases based on the greenhouse monitoring video. It presents a method that combines the visual saliency and online clustering to extract the key frame from greenhouse vegetables monitoring video. Firstly X2 histograms are used to measure the similarity of each frame to the first frame, which eliminates the meaningless frames and improve data processing efficiency and costs. Then, all frames will be converted to HSV color space and a saliency map of each frame is generated based on H component value and S component value. According to the saliency map, the salient region can be obtained. During the process of extracting the salient region, there is a possibility that the information of disease spots is lost. Therefore, morphological method would be utilized to restore the lost information. Finally, online clustering is performed to classify the salient regions into different clusters, and mean pixels value is used to select the key frames. The results indicate that this method can obtain information of entire leaf area of vegetables and extract the key frame effectively.