Broiler-Net: A Deep Convolutional Framework for Broiler Behavior
  Analysis in Poultry Houses
Tahereh Zarrat Ehsan Seyed Mehdi Mohtavipour
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