A target extraction method for 3D pig point clouds from a top-down perspective

Abstract

Currently, there are many studies focusing on keypoint extraction, weight estimation, and pose assessment using point clouds from pig backs. But extracting point clouds from complex environments remains challenging, especially when the data collection environment, location and height are changing. This study addresses the challenge of extracting 3D point clouds of pig in complex environments with variable heights and severe interference from a bird’s-eye perspective by proposing a target extraction method based on a single Time-of-Flight (TOF) depth camera. A custom-designed pushcart-based data acquisition equipment was utilized to collect 987 point cloud datasets under diverse conditions, encompassing three height levels to accommodate various pig body sizes and postures. A dynamic algorithm, dynamic point-cloud feature focusing and segmentation (DPFFS), was developed, which integrates a point counting peak statistical filtering module and a multi-dimensional perceptual spatial filtering module to remove ground point clouds and other interfering noise. This way of extracting target does not require pigs to move through specific channels and is also transferable to the segmentation of multiple targets, and other animals, and varied scenes. Experimental results show that the pig body point clouds were highly consistent with the ground truth, as represented by the manual segmentation results, with an average intersection over union (IoU) of 0.984, considering the erroneous segmentation caused by clustering, the IoU is 0.836, mis-segmentation rate is 0.17. After voxel grid downsampling, the DPFFS algorithm achieved an average running time of 0.928 s. It can serve as a pre-processing module for point cloud target extraction in various application scenario, providing accurate preliminary results for tasks such as individual identification, body size measurement, and pig weight estimation, etc.

Keywords

Depth camera / 3D point cloud / pig point cloud target extraction

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