Abstract
In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.
Highlights
The early detection of management problems related to health and welfare is an important aspect of caring for group-housed livestock
It is almost impossible for individual animals to be cared for by a small number of farm workers who work on a large-scale livestock farm
Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility [9,10,11]
Summary
The early detection of management problems related to health and welfare is an important aspect of caring for group-housed livestock. Caring for individual animals is necessary to minimize the possible damage caused by infectious diseases or other health and welfare problems. It is almost impossible for individual animals to be cared for by a small number of farm workers who work on a large-scale livestock farm. Several studies using surveillance techniques have recently been conducted to automatically monitor livestock, in what is known as “precision livestock farming” (PLF) [1] Several attached sensors, such as accelerometers, gyro sensors, and radio frequency identification (RFID) tags, are used to automate the management of livestock farms in examples of PLF [2].
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