This paper,in view of colleges’ R&D institUtion assessment of Hebeiprovince,builds DEA assessment model,and compares this model with the general evaluation method.
In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recentl...In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.展开更多
不正确的坐姿通常会导致青少年近视、脊柱侧弯和退行性疾病。研究能够快速、准确识别不规律坐姿的智能监测技术,有助于保持正确的姿势并预防健康问题。为了解决RGB图像易受光照强度以及遮挡因素的干扰并造成的识别率不高等问题,通过采...不正确的坐姿通常会导致青少年近视、脊柱侧弯和退行性疾病。研究能够快速、准确识别不规律坐姿的智能监测技术,有助于保持正确的姿势并预防健康问题。为了解决RGB图像易受光照强度以及遮挡因素的干扰并造成的识别率不高等问题,通过采用双流RGB-D图像作为双输入,利用ResNet网络中的残差结构改进EfficientNet基线网络结构,提出了一种基于改进R-EfficientNet的双流RGB-D多模态信息融合的坐姿识别方法。试验结果表明,提出的R-EfficientNet融合方法模型对8种坐姿的识别均值平均精度(mean average precision,mAP)达到了98.5%。与CNN、Vgg16、ResNet18、EfficientNet、RGB-D不同的输入方法相比,所提方法获得了最高的识别率。该方法不仅可以用于坐姿客观监测,具有医学和社会效益,此外还为人体工学研究者们提供改进办公家具的方案。展开更多
文摘This paper,in view of colleges’ R&D institUtion assessment of Hebeiprovince,builds DEA assessment model,and compares this model with the general evaluation method.
文摘In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.
文摘不正确的坐姿通常会导致青少年近视、脊柱侧弯和退行性疾病。研究能够快速、准确识别不规律坐姿的智能监测技术,有助于保持正确的姿势并预防健康问题。为了解决RGB图像易受光照强度以及遮挡因素的干扰并造成的识别率不高等问题,通过采用双流RGB-D图像作为双输入,利用ResNet网络中的残差结构改进EfficientNet基线网络结构,提出了一种基于改进R-EfficientNet的双流RGB-D多模态信息融合的坐姿识别方法。试验结果表明,提出的R-EfficientNet融合方法模型对8种坐姿的识别均值平均精度(mean average precision,mAP)达到了98.5%。与CNN、Vgg16、ResNet18、EfficientNet、RGB-D不同的输入方法相比,所提方法获得了最高的识别率。该方法不仅可以用于坐姿客观监测,具有医学和社会效益,此外还为人体工学研究者们提供改进办公家具的方案。
