kf_hungarian_tracker

This package implememts a multi-object tracker with Kalman filter and Hungarian algorithm. Hungarian algorithm is used to associate detection and previously known objects. For each object, a Kalman filter is maintained to track the position and velocity.

Hungarian algorithm

Hungarian algorithm is a combinatorial optimization algorithm that solves the assignment problem in polynomial time. Here we use an implementation from scipy.optimize.linear_sum_assignment. The default cost function is Euclidean distance between two object’s center. More cost functions like IoU of bounding boxes could be used.

Parameters

Parameters can be set in config/kf_hungarian.yaml. For more information on parameters for Kalman filter, check out KalmanFilter from OpenCV.

| parameters | Meaning | Default | | —————- | ————- | ——- | | global_frame | transform from pointcloud frame to global frame,
None means message frame is global | camera_link | | death_threshold | maximum missing frames before deleting an obstacle | 3 | | top_down | whether project 3D points on ground plane, set to do top-down tracking | False | | measurement_noise_cov | measurement noise for Kalman filter [x,y,z] | [1., 1., 1.] | | error_cov_post | initial posteriori error estimate covariance matrix [x,y,z,vx,vy,vz] | [1., 1., 1., 10., 10., 10.] | | process_noise_cov | model process noise covariance with estimated acceleration [ax,ay,az] | [2., 2., 0.5] | | vel_filter | minimum and maximum velocity to filter obstacles [min,max] (m/s) | [0.1, 2.0] | | height_filter | minimum and maximum height (z) to filter obstacles [min,max] (m) | [-2.0, 2.0] | | cost_filter | filter Hungarian assignments with cost greater than threshold (unit is m for Euclidean cost function) | 1.0 |

• The default units are m and m/s, could be changed according to the detection.