Depth-Based Detection of Standing-Pigs in Moving Noise Environments

• Main Authors: Jinseong Kim, Yeonwoo Chung, Younchang Choi, Jaewon Sa, Heegon Kim, Yongwha Chung, Daihee Park, Hakjae Kim
• Format: Article
• Language: English
• Published: MDPI AG 2017-11-01
• Series: Sensors
• Subjects: agriculture IT; computer vision; foreground detection; depth information; moving noise
• Online Access: https://www.mdpi.com/1424-8220/17/12/2757

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.