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:
this https URL

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

Broiler-Net: A Deep Convolutional Framework for Broiler Behavior  Analysis in Poultry Houses

Estimation eye gaze direction is useful in various human-computer interaction
tasks. Knowledge of gaze direction can give valuable information regarding
users point of attention. Certain patterns of eye movements known as eye
accessing cues are reported to be related to the cognitive processes in the
human brain. We propose a real-time framework for the classification of eye
gaze direction and estimation of eye accessing cues. In the first stage, the
algorithm detects faces using a modified version of the Viola-Jones algorithm.
A rough eye region is obtained using geometric relations and facial landmarks.
The eye region obtained is used in the subsequent stage to classify the eye
gaze direction. A convolutional neural network is employed in this work for the
classification of eye gaze direction. The proposed algorithm was tested on Eye
Chimera database and found to outperform state of the art methods. The
computational complexity of the algorithm is very less in the testing phase.
The algorithm achieved an average frame rate of 24 fps in the desktop
environment.

提出实时框架及卷积神经网络用于分类人眼凝视方向和评估眼部访问线索，并使用改进的 Viola-Jones 算法检测人脸，通过几何关系和面部特征点得到眼部区域，并进行逐帧测试以获得平均帧速率 24fps，结果显示其优于现有方法。