Abstract
In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.
Highlights
Object tracking is defined as the method of following one or more objects over a sequence of time steps
Since shape and orientation of the same moving object, either rigid or non-rigid, in neighboring frames is unlikely to change dramatically, we propose a novel point cloud descriptor to reflect the similarity between objects in neighboring frames, named Oriented Ensemble Shape Functions (OESF)
We propose a novel model-free detection-based tracking approach, Multiple Hypothesis Tracking (MHT)-Point Cloud Descriptor (PCD)-Speed, to detect and track moving objects in Doppler LiDAR scans
Summary
Object tracking is defined as the method of following one or more objects over a sequence of time steps. To detect an object from point clouds, we can use either model-based approaches or model-free methods [9]. [14] uses a cluster detector to detect moving objects in LiDAR scans and tracks them with an unscented Kalman filter and nearest neighbor data association methods. Doppler LiDAR can collect precise 3D spatial information and relative speed between the moving objects and the sensor on the beam direction, from which we can directly get the motion cue with high precision. We take advantages of Doppler LiDAR and develop a model-free, detection-based tracking approach for moving objects. The moving direction is estimated using the updated speed state from Kalman filter. Afterwards, based on the predicted and updated motion state, track hypotheses are proposed by performing gating as discussed below
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