Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects – which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is benchmarked against a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  M. Kolehmainen,et al.  Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines , 2009 .

[4]  M Kujala,et al.  A probabilistic neural network model for lameness detection. , 2007, Journal of dairy science.

[5]  T. Leroy,et al.  Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis , 2008 .

[6]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  L. Plümer,et al.  Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior , 2012 .

[8]  Daniel Berckmans,et al.  Automatic detection of lameness in dairy cattle-Vision-based trackway analysis in cow's locomotion , 2008 .

[9]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[10]  C Kamphuis,et al.  Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness. , 2013, Journal of dairy science.

[11]  Claudia Bahr,et al.  Original paper: Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques , 2010 .

[12]  Horst Bischof,et al.  MIForests: Multiple-Instance Learning with Randomized Trees , 2010, ECCV.

[13]  Ming-Hsuan Yang,et al.  Hierarchical Convolutional Features for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[17]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[18]  J. Hernández,et al.  Effect of lameness on the calving-to-conception interval in dairy cows. , 2001, Journal of the American Veterinary Medical Association.

[19]  Y T Gröhn,et al.  The effect of lameness on milk production in dairy cows. , 2001, Journal of dairy science.

[20]  J. Jaśkowski,et al.  Behaviour of lame cows: a review. , 2018 .

[21]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  L. Alban,et al.  Lameness in tied Danish dairy cattle: the possible influence of housing systems, management, milk yield, and prior incidents of lameness , 1996 .

[23]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[24]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.