Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows

Viazzi, Stefano; Bahr, Claudia; Hertem, T. van; Schlageter-Tello, A.; Romanini, Carlos Eduardo Bites; Halachmi, I.; Lokhorst, C.; Berckmans, Daniël

doi:10.1016/j.compag.2013.11.005

Cited by 134 publications

(71 citation statements)

References 30 publications

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“…Detailed video filtering procedures were described by Romanini et al (2013). The algorithm, as described by Viazzi et al (2014), was used to extract animal-based measures relevant for lameness detection: it automatically segments the cow body in the images, extracts the back spine contour line and calculates the walking speed and back curvature of the cow. The walking speed is quantified as the displacement along the walking direction of the cows of the bounding box surrounding the cow body.…”

Section: Farm and Managementmentioning

confidence: 99%

“…Studies using computer vision focus on gait analysis such as step overlap and hoof release angles (Pluk et al, 2012), and posture analysis such as back curvature Van Hertem et al, 2014;Viazzi et al, 2014). Back curvature can be extracted from images by different feature variables, such as an inverse radius Viazzi et al, 2014), different curvature angles ) and a back posture measurement (BPM) . The strong relation between back arch and gait abnormality is proven (Sprecher et al, 1997;Blackie et al, 2013;Hoffman et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

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The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC) = 0.757 ± 0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate videobased system (AUC = 0.732 ± 0.011) performed better than the multivariate milk sensors (AUC = 0.604 ± 0.026) and the multivariate behaviour sensors (AUC = 0.633 ± 0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC = 0.669 ± 0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ 2 , the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1% ± 4.7%) and specificity (83.2% ± 2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

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Section: Farm and Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

Self Cite

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“…However, automated methods such as [5,6] present a coarse-grained level for the ground truth locomotion/lameness score (LS). Hence, it is not possible to ascertain the sensitivity towards minor deviations from healthy gaits in above state-of-the-art methods.…”

Section: Introductionmentioning

confidence: 99%

“…from LS 1 to LS 2 in a 5-level LS system. We have developed a different approach as compared to both [5,6] by fitting a cubic polynomial on the extracted spine region from the 3D depth map. We also detect the neck area in order to eliminate neck movements' effect on the back arch.…”

Section: Introductionmentioning

confidence: 99%

Overhead spine arch analysis of dairy cows from three-dimensional video

Jabbar

Hansen

Smith

et al. 2017

Eighth International Conference on Graphic and Image Processing (ICGIP 2016)

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We present a spine arch analysis method in dairy cows using overhead 3D video data. This method is aimed for early stage lameness detection. That is important in order to allow early treatment; and thus, reduce the animal suffering and minimize the high forecasted financial losses, caused by lameness. Our physical data collection setup is non-intrusive, covert and designed to allow full automation; therefore, it could be implemented on a large scale or daily basis with high accuracy. We track the animal's spine using shape index and curvedness measure from the 3D surface as she walks freely under the 3D camera. Our spinal analysis focuses on the thoracic vertebrae region, where we found most of the arching caused by lameness. A cubic polynomial is fitted to analyze the arch and estimate the locomotion soundness. We have found more accurate results by eliminating the regular neck/head movements' effect from the arch. Using 22-cow data set, we are able to achieve an early stage lameness detection accuracy of 95.4%.

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“…Additionally, large differences in depth measurements depending on the fur color occurred (Salau et al, 2015). Viazzi et al (2014) compared the Microsoft Kinect 1 3D camera mounted in top view position to a 2D camera in side view position with regard to their applicability in lameness detection. Afterwards, the algorithms were improved and the classification results were optimized in Van Hertem et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Salau

Haas

Thaller

et al. 2016

animal

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Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore-or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.Keywords: dairy cattle, monitoring system, 3D camera, object recognition, wavelet transform ImplicationThe prototype of a multi-Kinect system is presented that captures 3D data of dairy cows in motion for health monitoring. When fully developed, it will provide an inexpensive means of automated early lameness detection and livestock monitoring, thereby helping to increase animal welfare. The large amount of recorded data will provide a solid basis for scientific analyses and can help to deepen the understanding of connections between traits and genetics. Object recognition and methods to analyze the animals' surfaces from the 3D data are important developmental steps that are approached using the wavelet transform. IntroductionSeveral camera-based studies were conducted over the last years yielding high rates of correct classification in lameness detection in dairy cows. Song et al. (2008), Pluk et al. (2012) and Viazzi et al. (2013) used digital 2D cameras to record walking cows from the side at distances of 3 to 6 m and analyzed step overlap, fetlock joints' angles or back posture. There were also several camera-based studies dealing with body condition score (BCS) determination. Azzaro et al. (2011) used linear and polynomial kernel principal analysis to reconstruct cows' shapes. Bercovich et al. (2012) presented BCS-prediction models that were based on five anatomical points. In both studies the animals were observed from a top view position using 2D cameras. The definitio...

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Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows

Cited by 134 publications

References 30 publications

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Overhead spine arch analysis of dairy cows from three-dimensional video

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

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