Abstract: Abstract: The use and wide application of video monitoring and control systems in pig pens is necessary for the automation and analysis of intelligence to improve the development tendencies of the pig-raising industry. The complete profiling of a pig is convenient to behavior analysis and to judge if it is sick. A pig's wandering causes the lack of its full profile being captured on video. For example, only part of the body or no body at all in the image when intelligently monitoring a single pig in a pigpen will result in an abnormal profile. Not all profiles, or camera angles, of the pig are efficient for behavior analysis in the case that the pig's profile is not fully revealed, such as the pig directly facing the camera. Only scenes with the fully exposed profile of the pig are convenient for observing and analyzing the symptoms of the pig. Therefore, it is essential to automatically segment the video that is monitoring the pigpen. A novel method is proposed for setting the frame attributes based on the level of exposure to a pig's profile in a standing posture. Two types of attributes are presented for each frame in the video recording, which represent the applicable and non-applicable profile of a pig. A pig's profile feature vector is calculated for differentiating the attributes after obtaining a profile of a single pig by image processing. The vector is composed of both the aspect ratio of the pig's external shape and a group of low frequency Fourier coefficients based on the pig's contour. The aspect ratio varies with the angle between the axis of the pig body and the horizontal line on the left side of the ground. The result of the test shows that the aspect ratio index is ideal and plays a significant role only in both the acute angle areas, i.e., ?30?, 30? and ?150?, 150?. Eight low frequency Fourier coefficients are also verified enough to describe the characteristic shape of the pig body in reconstructing the profile image of a single pig by the Fourier inversion method. Means and variances of both the feature vectors of the applicable and non-applicable profile contours are obtained from the sample training set. The category of the unknown frame is identified by the Mahalanobis distance discrimination from the testing video. The results showed that 91.7% of frames in the pig's profile could be properly recognized. Therefore, this method of producing the frame attributes based on a single pig's contour is reliable. This study may be helpful for subsequently analyzing behavior of a single pig in suspected cases.