Pose Estimation of Swimming Fish Using NACA Airfoil Model for Collective Behavior Analysis

We propose a pose estimation method using a National Advisory Committee for Aeronautics (NACA) airfoil model for fish schools. This method allows one to understand the state in which fish are swimming based on their posture and dynamic variations. Moreover, their collective behavior can be understood based on their posture changes. Therefore, fish pose is a crucial indicator for collective behavior analysis. We use the NACA model to represent the fish posture; this enables more accurate tracking and movement prediction owing to the capability of the model in describing posture dynamics. To fit the model to video data, we first adopt the DeepLabCut toolbox to detect body parts (i.e., head, center, and tail fin) in an image sequence. Subsequently, we apply a particle filter to fit a set of parameters from the NACA model. The results from DeepLabCut, i.e., three points on a fish body, are used to adjust the components of the state vector. This enables more reliable estimation results to be obtained when the speed and direction of the fish change abruptly. Experimental results using both simulation data and real video data demonstrate that the proposed method provides good results, including when rapid changes occur in the swimming direction.

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Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6797 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11354-11361

Author(s):

Jia Li ◽

Wen Su ◽

Zengfu Wang

Keyword(s):

Pose Estimation ◽

State Of The Art ◽

Body Part ◽

The State ◽

Body Parts ◽

Average Precision ◽

Bottom Up ◽

Assignment Algorithm

We rethink a well-known bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an improved stacked hourglass network with attention mechanisms, (3) a novel focal L2 loss which is dedicated to “hard” keypoint and keypoint association (body part) mining, and (4) a robust greedy keypoint assignment algorithm for grouping the detected keypoints into individual poses. Our approach not only works straightforwardly but also outperforms the baseline by about 15% in average precision and is comparable to the state of the art on the MS-COCO test-dev dataset. The code and pre-trained models are publicly available on our project page1.

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Ridge body parts features for human pose estimation and recognition from RGB-D video data

Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) ◽

10.1109/icccnt.2014.6963015 ◽

2014 ◽

Cited By ~ 22

Author(s):

Ahmad Jalal ◽

Yeonho Kim ◽

Daijin Kim

Keyword(s):

Pose Estimation ◽

Video Data ◽

Human Pose Estimation ◽

Body Parts ◽

Human Pose

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Economic Way of Thought and Legislationon Nonprofit Organizations

Voprosy Ekonomiki ◽

10.32609/0042-8736-2012-8-48-63 ◽

2012 ◽

pp. 48-63

Author(s):

L. Yakobson

Keyword(s):

Nonprofit Organizations ◽

Empirical Data ◽

Nonprofit Sector ◽

Social Medium ◽

The State ◽

Rapid Changes ◽

Comparative Advantages ◽

Essential Prerequisite

The article considers proper legislation as an essential prerequisite for actualization of NPOs comparative advantages. Restrictions imposed on NPOs are reasonable if they are compensated by benefits from greater trust. The rigidity of constrains and requirements should be optimized while accounting for peculiarities of a social medium, the state of the nonprofit sector, and the governments readiness to encourage the development of the latter. As empirical data suggests, Russian NPOs being on different stages of maturity need separate legal treatment. In the meanwhile, interests that prevail in the NPOs community are not always conducive to rapid changes.

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Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340847 ◽

2020 ◽

Author(s):

Alberto Favaro ◽

Riccardo Secoli ◽

Ferdinando Rodriguez y Baena ◽

Elena De Momi

Keyword(s):

Pose Estimation ◽

Estimation Method ◽

Steerable Needle

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A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

Applied Sciences ◽

10.3390/app11094241 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4241

Author(s):

Jiahua Wu ◽

Hyo Jong Lee

Keyword(s):

Pose Estimation ◽

Human Body ◽

State Of The Art ◽

Estimation Method ◽

Bottom Up ◽

Center Point ◽

Novel Approach ◽

Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

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