Robin Aly

Eric Arazo, Robin Aly, Kevin McGuinness

Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses. Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows passing through a hallway exiting a milking station and annotated the degree of lameness of the cows. This paper explores the resulting dataset and provides a detailed description of the data collection process. Additionally, we proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition: "healthy" or "lame." We improve this approach by forcing the model to focus on the structure of the cow, which we achieve by substituting the RGB videos with binary segmentation masks predicted with a trained segmentation model. This work aims to encourage research and provide insights into the applicability of computer vision models for cow lameness detection on farms.

Robin Aly, Maria Eskevich, Roeland Ordelman et al.

This report describes metrics for the evaluation of the effectiveness of segment-based retrieval based on existing binary information retrieval metrics. This metrics are described in the context of a task for the hyperlinking of video segments. This evaluation approach re-uses existing evaluation measures from the standard Cranfield evaluation paradigm. Our adaptation approach can in principle be used with any kind of effectiveness measure that uses binary relevance, and for other segment-baed retrieval tasks. In our video hyperlinking setting, we use precision at a cut-off rank n and mean average precision.