Automatic monitoring of pig locomotion using image analysis

Abstract

a b s t r a c tThe purpose of this study was to investigate the feasibility and validity of an automated image processing method to detect the locomotion of pigs in a group housed environment and under experimental conditions. Topview video images were captured for forty piglets, housed ten per pen. On average, piglets had a weight of 27 kg (SD ¼ 4.4 kg) at the start of experiments and 40 kg (SD ¼ 6.5) at the end. Each pen was monitored by a topview CCD camera. The image analysis protocol to automatically quantify locomotion involved localising pigs through background subtraction and tracking them over a set period of time. To validate the accuracy of detecting pigs "In Locomotion" or "Not In Locomotion", they were compared to offline manually labelled behavioural data ("In Locomotion" versus "Not In Locomotion"). This is the first study to show that the locomotion of pigs in a group can be determined using image analysis with an accuracy of 89.8%. Since locomotion is known to be associated with issues such as lameness, careful monitoring can give an accurate indication of the health and welfare of pigs.

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique

“…Initial attempts were made to explore Otsu's method for segmentation, since this has previously been successful in some pig pen scenarios (Otsu, 1979;Kashiha et al, 2014;Ott et al, 2014).…”

Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation

“…However, for video sequences of topview group-housed pigs with complicated backgrounds (such as those with light changes; urine stains, water stains, manure, and other objects on the ground; slow pig movement patterns; and varying colours of foreground objects), effective object extraction is still challenging and should be further researched. The higher the pen density, the more difficult segmenting the pigs in the image will be (Kashiha, Bahr, Ott, Moons, Niewold, Tuyttens et al, 2014).…”

“…This method can overcome the influence of sudden light changes, dynamic backgrounds, and motionless foreground objects. Kashiha, Bahr, Ott, Moons, Niewold, Tuyttens et al (2014) automatically monitored pig locomotion through image analysis methods such as binarization, morphological processing, and ellipse fitting.…”

Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation

“…This method can overcome the influence of sudden light changes, dynamic backgrounds, and motionless foreground objects. For environments lacking sufficient a priori knowledge, research on effective foreground object detection methods for video sequences of group-housed pigs in complex scenes remains a challenge (Kashiha et al, 2014).…”