Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity

• URL: http://arxiv.org/abs/2306.07879v2
• Date: Sat, 30 Sep 2023 15:12:07 GMT
• Title: Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
• Authors: Mu Zhou and Lucas Stoffl and Mackenzie Weygandt Mathis and Alexander Mathis
• Abstract summary: Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. We propose a novel pipeline called bottom-up conditioned top-down pose estimation. We demonstrate the performance and efficiency of our approach on animal and human pose estimation benchmarks.
• Score: 46.10812760258666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequent interactions between individuals are a fundamental challenge for pose estimation algorithms. Current pipelines either use an object detector together with a pose estimator (top-down approach), or localize all body parts first and then link them to predict the pose of individuals (bottom-up). Yet, when individuals closely interact, top-down methods are ill-defined due to overlapping individuals, and bottom-up methods often falsely infer connections to distant bodyparts. Thus, we propose a novel pipeline called bottom-up conditioned top-down pose estimation (BUCTD) that combines the strengths of bottom-up and top-down methods. Specifically, we propose to use a bottom-up model as the detector, which in addition to an estimated bounding box provides a pose proposal that is fed as condition to an attention-based top-down model. We demonstrate the performance and efficiency of our approach on animal and human pose estimation benchmarks. On CrowdPose and OCHuman, we outperform previous state-of-the-art models by a significant margin. We achieve 78.5 AP on CrowdPose and 48.5 AP on OCHuman, an improvement of 8.6% and 7.8% over the prior art, respectively. Furthermore, we show that our method strongly improves the performance on multi-animal benchmarks involving fish and monkeys. The code is available at https://github.com/amathislab/BUCTD

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