A computer vision approach to monitor activity in commercial broiler chickens using trajectory-based clustering analysis

[Display omitted] •A camera system is presented to monitor broiler activity using 2D computer vision.•A convolutional neural network-based chicken detector was developed.•Individual birds were tracked using a minimum cost network flow based tracker.•A clustering algorithm was applied to group trajectories into 3 activity clusters.•The potential of the system to monitor short-term changes in activity is discussed. To monitor changes in broiler behaviour related to animal health and welfare, farmers typically observe their flocks using manual observation. However, due to the labour intensive and continuous aspect of this task, the analysis of broiler behaviour could be automated using camera technology. This paper proposes a proof-of-concept camera surveillance system based on the automated detection and tracking of broilers to monitor activity bouts using unsupervised 2D trajectory clustering. Firstly, a convolutional neural network-based detector was trained and tested on our labelled dataset which resulted in a precision, recall and f score of 0.98, 0.90 and 0.94, respectively. Using a tracking-by-detection approach, the proposed system was able to track chickens across video frames with a multi-object tracking accuracy of 74.7%. A component-based feature saliency Gaussian mixture model (CFSGMM) was subsequently generated and applied to objectively cluster the trajectories based on their spatiotemporal information. Nineteen features were extracted from the trajectories, representing both static and dynamic characteristics of broiler movement, and three activity classes were identified: ‘least active/resting,’ ‘active’ and ‘highly-active.’ The proposed method was validated on one-minute monocular video sequences. CFSGMM was applied to cluster 2D trajectories relating to broiler activity bouts within the commercial rearing environment with an agreement ranging from 6.0 to 99.7% when compared to human observation. We demonstrate the potential of the computer vision system to monitor overt, short-term changes in broiler activity associated with on-farm events and discuss the opportunities of leveraging the technology to monitor longer term changes in welfare state. It is anticipated that further development of the detection and tracking systems will improve the performance of the trajectory clustering method.