Vision-Based Quadruped Pose Estimation and Gait Parameter Extraction Method

Gong, Zewu; Zhang, Yunwei; Lü, Dagang; Wu, Tiannan

doi:10.3390/electronics11223702

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Reference [39] introduced the RFB-HRNet algorithm and designed a framework consisting of three parts: target screening, animal pose estimation model, and extraction of animal gait parameters for quadruped animals. First, target screening is performed using object detection algorithms.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Li,

Sun,

Fan

et al. 2023

Agriculture

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Accurate cattle pose estimation is essential for Precision Livestock Farming (PLF). Computer vision-based, non-contact cattle pose estimation technology can be applied for behaviour recognition and lameness detection. Existing methods still face challenges in achieving fast cattle pose estimation in complex scenarios. In this work, we introduce the FasterNest Block and Depth Block to enhance the performance of cattle pose estimation based on the RTMPose model. First, the accuracy of cattle pose estimation relies on the capture of high-level image features. The FasterNest Block, with its three-branch structure, effectively utilizes high-level feature map information, significantly improving accuracy without a significant decrease in inference speed. Second, large kernel convolutions can increase the computation cost of the model. Therefore, the Depth Block adopts a method based on depthwise separable convolutions to replace large kernel convolutions. This addresses the insensitivity to semantic information while reducing the model’s parameter. Additionally, the SimAM module enhances the model’s spatial learning capabilities without introducing extra parameters. We conducted tests on various datasets, including our collected complex scene dataset (cattle dataset) and the AP-10K public dataset. The results demonstrate that our model achieves the best average accuracy with the lowest model parameters and computational requirements, achieving 82.9% on the cattle test set and 72.0% on the AP-10K test set. Furthermore, in conjunction with the object detection model RTMDet-m, our model reaches a remarkable inference speed of 39FPS on an NVIDIA GTX 2080Ti GPU using the PyTorch framework, making it the fastest among all models. This work provides adequate technical support for fast and accurate cattle pose estimation in complex farm environments.

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Section: Discussionmentioning

confidence: 99%

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Li,

Sun,

Fan

et al. 2023

Agriculture

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“…On the other hand, a method that feeds back the gait patterns of a spider-type robot based on the terrain detected with an RGB-D vision system is proposed in [ 15 ]. At the same time, Gong proposes a method for extracting the gait patterns of quadruped animals based on their pose [ 48 ]. Chen proposes a method for pattern matching a robot with legs–wheels [ 49 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation

Cruz Ulloa,

Sánchez,

Del Cerro

et al. 2023

Biomimetics

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Robots with bio-inspired locomotion systems, such as quadruped robots, have recently attracted significant scientific interest, especially those designed to tackle missions in unstructured terrains, such as search-and-rescue robotics. On the other hand, artificial intelligence systems have allowed for the improvement and adaptation of the locomotion capabilities of these robots based on specific terrains, imitating the natural behavior of quadruped animals. The main contribution of this work is a method to adjust adaptive gait patterns to overcome unstructured terrains using the ARTU-R (A1 Rescue Task UPM Robot) quadruped robot based on a central pattern generator (CPG), and the automatic identification of terrain and characterization of its obstacles (number, size, position and superability analysis) through convolutional neural networks for pattern regulation. To develop this method, a study of dog gait patterns was carried out, with validation and adjustment through simulation on the robot model in ROS-Gazebo and subsequent transfer to the real robot. Outdoor tests were carried out to evaluate and validate the efficiency of the proposed method in terms of its percentage of success in overcoming stretches of unstructured terrains, as well as the kinematic and dynamic variables of the robot. The main results show that the proposed method has an efficiency of over 93% for terrain characterization (identification of terrain, segmentation and obstacle characterization) and over 91% success in overcoming unstructured terrains. This work was also compared against main developments in state-of-the-art and benchmark models.

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“…This method of identifying gait through local modeling is easier to verify quantitatively. Some researchers have achieved gait recognition by studying the distribution patterns of position changes of body skeletal points [ 18 , 19 , 20 ]. For example, using Microsoft’s Kinect, a video stream with the distribution of human skeletal points is output directly from the original video stream.…”

Section: Introductionmentioning

confidence: 99%

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition

Sun

Zhao

et al. 2023

Entropy

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Gait recognition is one of the important research directions of biometric authentication technology. However, in practical applications, the original gait data is often short, and a long and complete gait video is required for successful recognition. Also, the gait images from different views have a great influence on the recognition effect. To address the above problems, we designed a gait data generation network for expanding the cross-view image data required for gait recognition, which provides sufficient data input for feature extraction branching with gait silhouette as the criterion. In addition, we propose a gait motion feature extraction network based on regional time-series coding. By independently time-series coding the joint motion data within different regions of the body, and then combining the time-series data features of each region with secondary coding, we obtain the unique motion relationships between regions of the body. Finally, bilinear matrix decomposition pooling is used to fuse spatial silhouette features and motion time-series features to obtain complete gait recognition under shorter time-length video input. We use the OUMVLP-Pose and CASIA-B datasets to validate the silhouette image branching and motion time-series branching, respectively, and employ evaluation metrics such as IS entropy value and Rank-1 accuracy to demonstrate the effectiveness of our design network. Finally, we also collect gait-motion data in the real world and test them in a complete two-branch fusion network. The experimental results show that the network we designed can effectively extract the time-series features of human motion and achieve the expansion of multi-view gait data. The real-world tests also prove that our designed method has good results and feasibility in the problem of gait recognition with short-time video as input data.

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Vision-Based Quadruped Pose Estimation and Gait Parameter Extraction Method

Cited by 3 publications

References 28 publications

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Deep Learning Vision System for Quadruped Robot Gait Pattern Regulation

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition

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