基于时空特征学习的视频鸡行为识别与定位

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Yilei Hu , Jinyang Xu , Zhichao Gou , Di Cui
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引用次数: 0

摘要

及时获取鸡的行为信息对评估鸡的健康状况和生产性能至关重要。基于视频的行为识别由于其准确性和鲁棒性而成为获取此类信息的主要技术。基于视频的模型通常从固定时长的单个视频片段中预测单个行为。然而,在家禽的高活动期间,行为转变可能发生在视频片段中,现有模型通常无法有效捕捉这种转变。这一限制突出了基于视频的行为识别模型的时间分辨率不足。提出了一种基于时空特征学习的鸡行为识别与定位模型CBLFormer。该模型旨在识别视频片段中过渡前后发生的行为,并为每个行为定位相应的时间间隔。将改进的变压器块、级联编码器-解码器网络(CEDNet)、基于变压器的磁头和加权距离交联(WDIoU)损失集成到CBLFormer中,以增强模型区分不同行为类别和定位行为边界的能力。为了训练和测试CBLFormer,通过收集320只鸡不同年龄和饲养密度的视频,创建了一个数据集。结果表明,CBLFormer在98.34%的测试集中实现了[email protected]:0.95。集成CEDNet对CBLFormer的性能提升贡献最大。可视化结果证实,该模型有效捕获了鸡的行为边界,正确识别了鸡的行为类别。迁移学习结果表明,该模型适用于现实家禽养殖场中鸡的行为识别和定位任务。该方法处理了家禽行为在视频片段内发生转变的情况,并提高了基于视频的行为识别模型的时间分辨率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning
Timely acquisition of chicken behavioral information is crucial for assessing chicken health status and production performance. Video-based behavior recognition has emerged as a primary technique for obtaining such information due to its accuracy and robustness. Video-based models generally predict a single behavior from a single video segment of a fixed duration. However, during periods of high activity in poultry, behavior transition may occur within a video segment, and existing models often fail to capture such transitions effectively. This limitation highlights the insufficient temporal resolution of video-based behavior recognition models. This study presents a chicken behavior recognition and localization model, CBLFormer, which is based on spatiotemporal feature learning. The model was designed to recognize behaviors that occur before and after transitions in video segments and to localize the corresponding time interval for each behavior. An improved transformer block, the cascade encoder-decoder network (CEDNet), a transformer-based head, and weighted distance intersection over union (WDIoU) loss were integrated into CBLFormer to enhance the model's ability to distinguish between different behavior categories and locate behavior boundaries. For the training and testing of CBLFormer, a dataset was created by collecting videos from 320 chickens across different ages and rearing densities. The results showed that CBLFormer achieved a [email protected]:0.95 of 98.34 % on the test set. The integration of CEDNet contributed the most to the performance improvement of CBLFormer. The visualization results confirmed that the model effectively captured the behavioral boundaries of chickens and correctly recognized behavior categories. The transfer learning results demonstrated that the model is applicable to chicken behavior recognition and localization tasks in real-world poultry farms. The proposed method handles cases where poultry behavior transitions occur within the video segment and improves the temporal resolution of video-based behavior recognition models.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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