College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Sensors (Basel). 2020 Apr 22;20(8):2381. doi: 10.3390/s20082381.
The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig's behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig's multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.
不同种类猪行为的统计数据可以反映其健康状况。然而，传统的猪行为统计数据是通过人眼从视频中获取并记录的。为了减少劳动力和时间消耗，本文提出了一种基于 SlowFast 网络架构的具有时空卷积网络的猪行为识别网络，用于对五类行为进行分类。首先，通过从 3 个月不间断拍摄的视频中截取短片，构建了一个猪行为识别视频数据集 (PBVD-5)，该数据集由五类猪行为组成：进食、躺卧、运动、抓挠和交配。随后，提出了一种基于 SlowFast 网络的猪多行为识别时空卷积网络 (PMB-SCN)。实现了具有不同架构的网络的结果，并将最优架构与我们数据集的最先进的单流 3D 卷积网络进行了比较。我们的 3D 猪行为识别网络在 PBVD 的测试集上的 top-1 准确率为 97.63%，视图准确率为 96.35%，在一个全新的、从完全不同猪圈采集的测试集上的 top-1 准确率为 91.87%，视图准确率为 84.47%。实验结果表明，该网络具有出色的泛化能力，为后续的猪检测和行为识别提供了可能性。
