Abstract
A computer vision-based system for the automatic detection of dairy cow lying behaviour in free-stall barns is proposed. The system is composed of a multi-camera video-recording system and a software component which executes a cow lying behaviour detector model using the Viola–Jones algorithm. A method to carry out the training, testing and validation phase of the modelled cow lying behaviour detector is described. The performance of the system was tested in an area of a head-to-head free-stall barn where a group of 15 Holstein dairy cows was housed. A multi-camera video-recording system was installed to obtain panoramic top-view images of the area under study. Since the Viola–Jones algorithm was not invariant to the rotation of the cow images, two classifiers were modelled, one for each row of stalls located in the barn. These two classifiers were implemented in the software component of the system in order to perform the lying behaviour detection. The system was validated by comparing its detection results with those generated from visual recognition. The ability of the system to detect cow lying behaviour was confirmed by the high value of its sensitivity, which was approximately 92%. Conversely, the value of the branching factor which was approximately 0.08 indicated that one false positive was detected for every 13 well detected cows. These results suggest that the system proposed in this study could be used for the calculation of the cow lying index which is widely used to investigate cow lying behaviour in free-stall barns. ► To model the cow lying behaviour detector image enhancement was not required. ► In the training phase FPR cas of the order of 10 −7 and TPR cas of 0.90 were achieved. ► The testing phase proved detector ability even when noise affected camera images. ► The cow lying behaviour detector provided a sensitivity of 92% and a BF of 0.08. ► CVBS can be used to compute cow lying index.
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