A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring
AbstractSegmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor. View Full-Text
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Ju, M.; Choi, Y.; Seo, J.; Sa, J.; Lee, S.; Chung, Y.; Park, D. A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring. Sensors 2018, 18, 1746.
Ju M, Choi Y, Seo J, Sa J, Lee S, Chung Y, Park D. A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring. Sensors. 2018; 18(6):1746.Chicago/Turabian Style
Ju, Miso; Choi, Younchang; Seo, Jihyun; Sa, Jaewon; Lee, Sungju; Chung, Yongwha; Park, Daihee. 2018. "A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring." Sensors 18, no. 6: 1746.
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