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Human Pose Estimation (HPE) is one of the fundamental problems in computervision. It has applications ranging from virtual reality, human behavioranalysis, video surveillance, anomaly detection, self-driving to medicalassistance. The main objective of HPE is to obtain the person's posture fromthe given input. Among different paradigms for HPE, one paradigm is calledbottom-up multi-person pose estimation. In the bottom-up approach, initially,all the key points of the targets are detected, and later in the optimizationstage, the detected key points are associated with the corresponding targets.This review paper discussed the recent advancements in bottom-up approaches forthe HPE and listed the possible high-quality datasets used to train the models.Additionally, a discussion of the prominent bottom-up approaches and theirquantitative results on the standard performance matrices are given. Finally,the limitations of the existing methods are highlighted, and guidelines of thefuture research directions are given.

Bottom-up approaches for multi-person pose estimation and it's applications: A brief review