Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning

11/16/2021
by   Samayan Bhattacharya, et al.
0

Animal pose estimation has recently come into the limelight due to its application in biology, zoology, and aquaculture. Deep learning methods have effectively been applied to human pose estimation. However, the major bottleneck to the application of these methods to animal pose estimation is the unavailability of sufficient quantities of labeled data. Though there are ample quantities of unlabelled data publicly available, it is economically impractical to label large quantities of data for each animal. In addition, due to the wide variety of body shapes in the animal kingdom, the transfer of knowledge across domains is ineffective. Given the fact that the human brain is able to recognize animal pose without requiring large amounts of labeled data, it is only reasonable that we exploit unsupervised learning to tackle the problem of animal pose recognition from the available, unlabelled data. In this paper, we introduce a novel architecture that is able to recognize the pose of multiple animals fromunlabelled data. We do this by (1) removing background information from each image and employing an edge detection algorithm on the body of the animal, (2) Tracking motion of the edge pixels and performing agglomerative clustering to segment body parts, (3) employing contrastive learning to discourage grouping of distant body parts together. Hence we are able to distinguish between body parts of the animal, based on their visual behavior, instead of the underlying anatomy. Thus, we are able to achieve a more effective classification of the data than their human-labeled counterparts. We test our model on the TigDog and WLD (WildLife Documentary) datasets, where we outperform state-of-the-art approaches by a significant margin. We also study the performance of our model on other public data to demonstrate the generalization ability of our model.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 5

page 6

02/06/2020

AnimePose: Multi-person 3D pose estimation and animation

3D animation of humans in action is quite challenging as it involves usi...
11/23/2021

Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept

Lifting the 2D human pose to the 3D pose is an important yet challenging...
11/29/2016

Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision

We propose a CNN-based approach for 3D human body pose estimation from s...
01/07/2017

Group Visual Sentiment Analysis

In this paper, we introduce a framework for classifying images according...
11/25/2017

Structure-Aware and Temporally Coherent 3D Human Pose Estimation

Deep learning methods for 3D human pose estimation from RGB images requi...
08/28/2021

AP-10K: A Benchmark for Animal Pose Estimation in the Wild

Accurate animal pose estimation is an essential step towards understandi...
09/06/2020

DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation

We present a novel approach to the garment animation problem through dee...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.