Abstract
Body cleanliness is considered an important indicator for evaluating cow welfare. At present, assessing the cleanliness of different cow body parts is considered as a subjective and labor-intensive task. Automatic body cleanliness scoring needs to start with body parts segmentation. Despite the fact that pattern recognition methods for human body detection and analysis have flourished in the last decade, computer vision analysis of cow body-part is scarce in literature, and most of cow body detection in video images recognizes and segments the cow as a whole, not using all of the body part information. This study presents a computer vision method that automatically identifies nine cow’s body parts, i.e. head, torso, udder, belly (or rear), left foreleg, right foreleg, left hindleg right hindleg and tail. We used a method for body parts detection using an improved version of template matching. The entire image process included video recording, image restoration and pre-processing, skeleton extraction, templates matching, recognition of right or left leg, calibration and body parts identification. 1421 side-view images of 113 cows and 859 back-view images of 75 cows were collected on a Chinese research dairy farm using an RGB-depth camera installed in the barn for Holstein lactating cows. The average body parts segmentation accuracies for side-view and back-view were 96% and 91%, respectively. The results indicate that it is possible to automatically detect and extract body parts from RGB-depth images without any manual interference.
Highlights
An important indicator for the welfare of cows is the cleanliness of the cow, as it is one of the critical factors influencing bacterial contamination of milk (Zucali et al, 2011), somatic cell count (SCC) and subclinical intra-mammary infection rate (Schreiner and Ruegg, 2003)
Cow whole body can be divided into nine parts: head, left foreleg (LFL), right foreleg (RFL), left hind leg (LHL), right hind leg (RHL), torso, udder, belly and tail
The 3D depth and color images are acquired by the Microsoft Kinect v2 sensor and used with the distance transform values to effectively classify the cow body-parts
Summary
An important indicator for the welfare of cows is the cleanliness of the cow, as it is one of the critical factors influencing bacterial contamination of milk (Zucali et al, 2011), somatic cell count (SCC) and subclinical intra-mammary infection rate (Schreiner and Ruegg, 2003). Cangar et al (2008), for instance, developed an automatic image-analysis system to identify the locomotion and posture behavior of pregnant cows prior to calving Their method records po sition and body-size measurements over time in order to classify behavior as, e.g., standing or lying, and eating or drinking. Zhao et al (2017) applied RF decision to segment eight body regions, i.e., head, neck, body, forelegs, hind legs and tail with local binary pattern (LBP)-based depth features to analyze cow behavior. This system concentrates on side-views and cannot detect body parts from other viewpoints, necessary for a proper evaluation of the cow cleanliness. We applied a template-based method for endpoints classification, Skeleton Path Similarity Matching (SPSM) (Bai et al.2008), which provided a robust description of the posture of cows, despite the variation in their appearance and relative pose to wards the camera
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