Articulated Human Pose Estimation Using Greedy Approach

Abstract

The goal of this Chapter is to introduce an efficient and standard approach for human pose estimation. This approach is based on a bottom up parsing technique which uses a non-parametric representation known as Greedy Part Association Vector (GPAVs), generates features for localizing anatomical key points for individuals. Taking leaf out of existing state of the art algorithm, this proposed algorithm aims to estimate human pose in real time and optimize its results. This approach simultaneously detects the key points on human body and associates them by learning the global context. However, In order to operate this in real environment where noise is prevalent, systematic sensors error and temporarily crowded public could pose a challenge, an efficient and robust recognition would be crucial. The proposed architecture involves a greedy bottom up parsing that maintains high accuracy while achieving real time performance irrespective of the number of people in the image.

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

“…Machine learning is an evolutionary area of algorithms, hardware and storage systems working in smarter ways for several applications, such as (a) abnormal behavior proactive detection for reasonable solutions in advance; (b) creating events models based on system training in order to forecast the values of a future inquiry; (c) testing the future inquiry based on the understating of the created event model and (d) computing the individual loss reserve [12]. Thus, different researchers have used the advantage of machine learning for automated wheat diseases classification, estimation of the long-term agricultural output and prediction of soil organic carbon and available phosphorus [13][14][15].…”