Counting cattle Dataset: Supporting livestock Multi Objetct Tracking

Abstract
Livestock farming has made technological advancements in recent times; however, there is still much to improve, as AI and computer vision have not yet fully penetrated the area. Livestock farming involves many routine tasks that require automation for improved control over production processes. Although the scientific community has made significant efforts to support precision livestock farming in recent years, artificial intelligence-based solutions for livestock counting and management remain limited (Myat Noe et al., 2023). Myat Noe et al. (2023) proposed a method for counting black cattle counting using the YOLO (You Only Look Once) object detection framework, in combination with the Detectron2 segmentation model and the DeepSORT (Wojke, Bewley and Paulus, 2017) and StrongSORT (Du et al., 2023) Multi Object Tracking (MOT) solutions. The study highlights the challenges faced in supporting cattle tracking as MOT solutions generally target objects with distinctly visual features. In the context of cattle detection and tracking, the requirements are more demanding due to the high visual similarity in color and shape among individuals. As a result, additional effort is still required to support precision agriculture through cattle identification and monitoring. This includes the creation of custom datasets for livestock MOT since existing public repositories do not meet the specific needs of this application. Moreover, retraining the models and applying additional techniques in combination are still necessary to achieve more satisfactory results (Myat Noe et al., 2023). In this context, the present dataset provides a series of videos and ground truth information for tracking Brangus cattle in a ranch environment in Uruguay. The dataset maintains consistent bounding boxes and identifiers for individual animals, being specifically useful for evaluating the effectiveness of tracking algorithms in support of cattle monitoring through MOT metrics.

DataRecords
The dataset contains 10 preselected videos, totaling a recording time of 3.17 minutes and a total of 93 cattle. It is organized into folders, one containing the videos used for tracking and another folder containing the ground truth, which consists of a CSV file with the processed information and the video on which the analysis was performed. The detection values were stored in the mentioned CSV file, so they can be processed later. Each line represents a detection, with the following information per line:
-The frame number corresponding to the detection
-The identifier number corresponding to the detected object
-The x-coordinate value of the top-left corner of the detected object
-The y-coordinate value of the top-left corner of the detected object
-The width of the bounding box
-The height of the bounding box
-The confidence of the detection

Further information on dataset construction and organization available on the dataset description attached file.