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Human Pose Estimation Benchmarking and Action Recognition Using AI

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Saikushwanth, V., 2021. Human Pose Estimation Benchmarking and Action Recognition Using AI. United International Journal for Research & Technology (UIJRT), 2(12), pp.90-98

Abstract

Existing frameworks for video-based posture assessment and following battle to perform well on reasonable recordings with various individuals and regularly neglect to yield body-present directions steady over the long haul. To address this inadequacy this paper presents PoseTrack which is another huge scope benchmark for video-based human posture assessment and verbalized following. Our new benchmark includes three assignments zeroing in on I) single-outline multi-individual posture assessment, ii) multi-individual posture assessment in recordings, and iii) multi-individual enunciated following. To set up the benchmark, we gather, explain and discharge another dataset that highlights recordings with various individuals marked with individual tracks and verbalized posture. A public brought together assessment worker is given to permit the examination local area to assess on a held-out test set. Moreover, we lead a broad trial concentrate on ongoing methodologies to explained present following and give examination of the qualities and shortcomings of the cutting edge. We imagine that the proposed benchmark will invigorate useful examination both by giving a huge and agent preparing dataset just as giving a stage to impartially assess and think about the proposed strategies.

Keywords: benchmark, dataset, Human Posture, Action recognition, Posture Dataset.

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