This package provides a monocular vision-based person tracking and identification framework for person following robots. It first detects people using tf-pose-estimation, and then track them in the robot space with Unscented Kalman filter with the ground plane information. The combination of Convolutional Channel Features and Online Boosting runs on the top of the tracking module to keep tracking the target person with a re-identification capability. The entire system is designed so that it can be run on a Jetson TX2/Xavier, and it can easily be reproduced and reused on a new mobile robot platform.
- dlib
- flann
- tensorflow
- tf-pose-estimation
- Quick test on a sample rosbag (Desktop PC with CUDA >= 10.2)
- Quick test using a webcam (Desktop PC with CUDA >= 10.2 / Jetson Xavier with Jetpack 4.5.1)
We tested this package on Xavier/Jetpack 4.5.1
Note: We recommend using docker to avod the painful installation process
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Kenji Koide, Jun Miura, and Emanuele Menegatti, Monocular Person Tracking and Identification with Online Deep Feature Selection for Person Following Robots, Robotics and Autonomous Systems [link].
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Kenji Koide and Jun Miura, Convolutional Channel Features-based Person Identification for Person Following Robots, 15th International Conference IAS-15, Baden-Baden, Germany, 2018 [link].