Predicting Human-Coordinate Orientation with Deep Neural Networks and a LSTM Recurrent Model


Predicting Human-Coordinate Orientation with Deep Neural Networks and a LSTM Recurrent Model – We develop an algorithm for the prediction of human-level visual odometry from video data of an individual pedestrian performing hand gestures in a stationary vehicle. The algorithm is a simple yet effective approach to improve the performance of machine learning algorithms for odometry detection. We prove the application of the algorithm to detecting human-level visual landmarks in videos, where we test how effective a hand gesture identification approach can be as a human gesture recognition technique.

In this paper, we provide an efficient and efficient way to estimate the position and orientation of an object relative to a human subject at the same time. We compare our method to previous works using a new and improved dataset of 675,000 object orientation-based videos, and we show that our algorithm provides accurate and flexible estimates.

This work presents a method allowing an information theoretic system model to extract high-dimensional representations of the data. We provide a principled, efficient algorithm for this task, and a methodology for optimizing the algorithm’s performance. We present a detailed study of the proposed algorithm, which shows that it yields significantly better performance on both synthetic and real data.

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Predicting Human-Coordinate Orientation with Deep Neural Networks and a LSTM Recurrent Model

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