Pedestrian skeleton tracking using openPose and probabilistic filtering

Abstract

An essential task to prevent pedestrian injuries by an autonomous vehicle is the ability to correctly detect and predict its movement. A deep learning-based 2D human poses detector, as OpenPose, provides a skeleton of people present in an image captured by cameras mounted in the car. Nevertheless, these kinds of algorithms give a frame solution but do not capture the movement between them. Then, parts of the body are missed or the skeleton leaped to another part of the image where the infrastructure resembles a person. In this context, an algorithm based in the Kalman Filter algorithm to estimate the real skeleton including correlations in time and between parts of the body is presented. The algorithm was tested on videos using data provided by a vehicle moving in real scenarios. Results are presented that shown the capability of the algorithm to correct the mentioned loss of tracking.