Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms. Github: https://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis

本文提出了一种用于分析禽舍中鸡类行为以检测异常行为的新型实时框架，具体研究了两种显著异常行为：不活跃的肉鸡和聚集行为。该提出的框架包括三个关键步骤：利用先进的深度学习模型进行鸡类检测，使用快速追踪模块跟踪相继帧中的个体鸡，以及在视频流中检测异常行为。实验研究被进行以评估所提出算法在准确评估鸡类行为方面的有效性。结果表明，我们的框架提供了一种精确高效的解决方案，可实时检测异常，有助于及时干预以维持鸡类健康并提高养禽场的总体生产能力。

肉鸡网：一种用于家禽舍内肉鸡行为分析的深度卷积框架