Multiple-target foreground detection method for look-down group-housed pigs in look-down state under complicated background

Abstract

The invention provides a multiple-target foreground detection method for group-housed pigs in a look-down state under a complicated background. The multiple-target foreground detection method comprises the followings steps: firstly collecting a video sequence of the group-housed pigs in the look-down state; subsequently, setting an 'effective area'; acquiring a foreground target through a proposed prediction mechanism-based Gaussian mixture model foreground detection algorithm; meanwhile, performing maximum entropy threshold segmentation by utilizing the color information of the foreground target so as to acquire another foreground target; finally, performing fusion on results of two algorithms and performing mathematical morphology processing to obtain a final foreground target. The initial background of the multiple-target foreground detection method provided by the invention does not need to be acquired in advance; the multiple-target foreground detection method is adaptable to the disturbance of foreign substances, such as illumination change, ground urine, water stain and manure, existing in the background; the motion pattern of pig individuals is that the pig individuals stop and go; the foreground target detection on the pig individuals under the complicated background that the foreground targets are various in color and the like is realized, and therefore the foundation is laid for further exploring identity recognition, behavior analysis and the like on the group-housed pig individuals.

Description

technical field [0001] The invention relates to machine vision technology, in particular to a method for extracting a video surveillance foreground target, in particular to a method for detecting the foreground of a pig individual target in a surveillance video of group pig raising under a bird's-eye view state. Background technique [0002] Accurate pig individual prospect detection is the basic work for subsequent research on pig individual identification, tracking, and behavior analysis. Most traditional methods use background subtraction and then binarization (see: Shao B, Xin H.A real-time computer vision assessment and control of thermal comfort for group-housed pigs[J].Computers and Electronics in Agriculture.2008,62( 1): 15-21.; Kashiha M, Bahr C, Haredasht S A, etc. The automatic monitoring of pigs water use by cameras[J]. Computers and Electronics in Agriculture, 2013: 164-169.), but such literature The foreground detection of individual pigs is not the focus of t...

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Problems solved by technology

Most traditional methods use background subtraction and then binarization (see: Shao B, Xin H.A real-time computer vision assessment and control of thermal comfort for group-housed pigs[J].Computers and Electronics in Agriculture.2008,62( 1): 15-21.; Kashiha M, Bahr C, Haredasht S A, etc. The automatic monitoring of pigs water use by cameras[J]. Computers and Electronics in Agriculture, 2013: 164-169.), but such literature The foreground detection of individual pigs is not the focus of the discussion, and the method is not ideal for the foreground detection of looking down on group pigs under complex backgrounds

In the literature specifically reporting the foreground detection of individual pigs (see: Ji Bin, Zhu Weixing et al. Pig house fixed camera background removal method [J]. Computer Application Research, 2011,28(9):3585-3589.; Tu G J, Karstoft H, Pedersen L J, et al. Foreground detection using loopy belief propagation [J]. Biosystems engineering, 2013, 116(1): 88-96.) also did not completely solve the background, no need for preset, complex background, etc. Looking down on the foreground of group pigs detection problem

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