Point Siamese Network for Person Tracking Using 3D Point Clouds.

Clicks: 287
ID: 76463
2019
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Abstract
Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.
Reference Key
cui2019pointsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cui, Yubo;Fang, Zheng;Zhou, Sifan;
Journal sensors
Year 2019
DOI
E143
URL
Keywords
convolutional neural networks lidar 3d person tracking

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