Abstract: With the decreasing cost of image sensor equipment, full-time monitoring has been gradually realized in the process of cattle breeding. Especially, in the whole life of cattle, the monitoring and analysis for cattle's behavior have become a research hotspot in the field of breeding. Acquiring a large amount of cattle image and video information, people are more concerned about how to process, analyze, understand and apply these data. How to segment dynamic objects from complex environment background is the precondition of cattle behavior analysis, and it is also the key of realizing long-distance, contactless and automatic detection for cattle behavior. The traditional machine vision image segmentation method is used to realize the clustering and extraction of pixels by artificially extracting image features. However, when the image background is complex, feature extraction will become very troublesome and even difficult to achieve. Deep Convolutional Neural Networks (DCNN) provides another solution, which enables computers to automatically learn and find the most descriptive and prominent features in each specific category of objects, and allows deep networks to discover potential patterns in various types of images. On the basis of massive labeled data, the accuracy of classification, segmentation, recognition and detection with convolutional neural network can be improved automatically through continuous training, and the labor cost is transferred from algorithm design to data acquisition, which reduces the difficulty of technology application. However, for cattle image segmentation, the complex breeding environment will be a problem. The color and texture of environmental information in the image will have an impact on the segmentation of cattle's details. Especially when FCN uses deconvolution operation in the process of up-sampling, it is insensitive to the details of the image and does not take into account the class relationship between the pixels, which makes the segmentation result lack of spatial regularity and spatial consistency, so the segmentation effect will be very rough. In order to improve the accuracy of semantics segmentation for fully convolutional networks and segmentation effect of cattle image details, this paper proposes a method of fully convolutional networks semantic segmentation based on RGBD cattle image. We create a concept which named "depth density". The value of depth density can quantify the probability about whether different pixels have the same category. According to the mapping relationship between RGB image and depth image on pixel level content, we optimize the semantic segmentation results of cattle's image by FCN. The experimental results showed that, better than FCN-8s, the proposed method could improve the pixel accuracy, mean accuracy, mean intersection over union and frequency weight intersection over union by 2.5%, 2.3%, 3.4% and 2.7% respectively.