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.
Kim et al. REF proposed a method to detect standing pigs using a Kinect camera, in which the temporal noise in the depth images is removed by applying spatiotemporal interpolation, then detection is achieved by applying edge detection based on the undefined depth values around pigs.
Depth-Based Detection of Standing-Pigs in Moving Noise Environments
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Medicine Computer Science Engineering