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제안 방법
- In [10], we proposed a “non-attached” method to detect estrus with an audio surveillance system. In this paper, we apply computer vision techniques to mounting detection for a large-scale farm to further improve the accuracy of estrus detection. It is difficult to segment the body of a Korean native cow from an input scene captured with a “tilted-down” camera because the color of the cow’s body in the scene is similar to the color of the background (i.
- The main idea of the proposed system is that we use a side-view camera and check the abrupt change (i.e., move-up by more than 40 cm within 0.5 seconds) of a mounting cow’s height with computer vision techniques. Typically, a mounting activity can be observed at 1.
- The principal idea (i.e., our main contribution) of the proposed system is that we utilize a side-view camera and check the abrupt change of a mounting cow’s height with computer vision techniques. As depicted in Fig.
- In this paper, we proposed a mounting detection method for Korean native cattle from video stream data with computer vision techniques and mounting detection rules. This study focused on detecting both spatial and temporal abnormalities of a mounting with a side-view camera. From the experiments, we confirmed that the proposed method could detect a mounting by considering the direction, magnitude, and history of the mounting motion, even in the case of a fence occlusion.
대상 데이터
- We set the resolution to 640 × 480 pixels and the frame rate to 24 frames/s (fps). The camera was located 2 m from the fence and 1.7 m above the ground, at Milyang and Sacheon farms. The RoI was set as a center area of 640 × 80 pixels from the acquired resolution 640 × 480 pixels (See Fig.
- With these settings, we acquired 25 mounting datasets from the observed field of size 5 m × 10 m. There were ten (Milyang farm) and five (Sacheon farm) cows in the field. From the experiment with 25 mounting datasets (mounting verification threshold Δ = 4, = 16, = 0.
이론/모형
- These two techniques can detect moving objects from the difference of each pixel between the previous frame and the current frame. For background subtraction, we use the Gaussian Mixture Model (GMM) method [15] that uses a parametric probability density function represented as a weighted sum of Gaussian component densities. To extract motion vectors, we select feature points such as the corners from each image frame.
- In this paper, we use the Motion History Image (MHI) method [13,14], which can provide rich motion information. Although some portion of a mounting behavior may be missed by a partial occlusion, we can utilize the motion history information to resolve the fence-occlusion problem.
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