A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation


A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation – Person re-identification is an important problem in many areas including robotics and artificial intelligence. In this paper, we investigate the challenge in Re-ID for the purpose of re-identification of the human-body connection from images. Following the previous work on this problem, we propose a novel two-phase re-identification algorithm based on the idea of re-scented image classification and localization. Under this framework, image re-ID is used to classify the human-body connection between the images. This paper considers re-ID as a supervised model which can easily be designed to re-identify the person and the person re-ID. The proposed re-ID algorithm is implemented using ImageNet, which handles image classification and localization for a semi-automated test and evaluation system. Furthermore, it is implemented using a machine learning framework which handles the classification and localization for an automatic re-ID system.

A task manifold is a set of a set of multiple instances of a given task. Existing work has been focused on learning the manifold from the input data. In this paper we describe our learning by simultaneously learning the manifold of the input and the manifold of the task being analyzed. The learning is done by using Bayesian networks to form a model of the manifold and perform inference. We illustrate the approach on a machine learning benchmark dataset and a real-world data based approach.

Generative Deep Episodic Modeling

Learning Discriminative Kernels by Compressing Them with Random Projections

A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation

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  • A Hierarchical Approach for Ground Based Hand Gesture Recognition

    Learning to Compose Task Multiple at OnceA task manifold is a set of a set of multiple instances of a given task. Existing work has been focused on learning the manifold from the input data. In this paper we describe our learning by simultaneously learning the manifold of the input and the manifold of the task being analyzed. The learning is done by using Bayesian networks to form a model of the manifold and perform inference. We illustrate the approach on a machine learning benchmark dataset and a real-world data based approach.


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