detection_by_tracker

Purpose

This package feeds back the tracked objects to the detection module to keep it stable and keep detecting objects.

The detection by tracker takes as input an unknown object containing a cluster of points and a tracker. The unknown object is optimized to fit the size of the tracker so that it can continue to be detected.

Inner-workings / Algorithms

The detection by tracker receives an unknown object containing a point cloud and a tracker, where the unknown object is mainly shape-fitted using euclidean clustering. Shape fitting using euclidean clustering and other methods has a problem called under segmentation and over segmentation.

Adapted from [3]

Simply looking at the overlap between the unknown object and the tracker does not work. We need to take measures for under segmentation and over segmentation.

Policy for dealing with over segmentation

1. Merge the unknown objects in the tracker as a single object.
2. Shape fitting using the tracker information such as angle and size as reference information.

Policy for dealing with under segmentation

1. Compare the tracker and unknown objects, and determine that those with large recall and small precision are under segmented objects.
2. In order to divide the cluster of under segmented objects, it iterate the parameters to make small clusters.
3. Adjust the parameters several times and adopt the one with the highest IoU.

Inputs / Outputs

Input

Name	Type	Description
~/input/initial_objects	tier4_perception_msgs::msg::DetectedObjectsWithFeature	unknown objects
~/input/tracked_objects	tier4_perception_msgs::msg::TrackedObjects	trackers

Output

Name	Type	Description
~/output	autoware_auto_perception_msgs::msg::DetectedObjects	objects

Parameters

Name	Type	Description	Default value
tracker_ignore_label.UNKNOWN	bool	If true, the node will ignore the tracker if its label is unknown.	true
tracker_ignore_label.CAR	bool	If true, the node will ignore the tracker if its label is CAR.	false
tracker_ignore_label.PEDESTRIAN	bool	If true, the node will ignore the tracker if its label is pedestrian.	false
tracker_ignore_label.BICYCLE	bool	If true, the node will ignore the tracker if its label is bicycle.	false
tracker_ignore_label.MOTORCYCLE	bool	If true, the node will ignore the tracker if its label is MOTORCYCLE.	false
tracker_ignore_label.BUS	bool	If true, the node will ignore the tracker if its label is bus.	false
tracker_ignore_label.TRUCK	bool	If true, the node will ignore the tracker if its label is truck.	false
tracker_ignore_label.TRAILER	bool	If true, the node will ignore the tracker if its label is TRAILER.	false

Assumptions / Known limits

(Optional) Error detection and handling

(Optional) Performance characterization

(Optional) References/External links

[1] M. Himmelsbach, et al. "Tracking and classification of arbitrary objects with bottom-up/top-down detection." (2012).

[2] Arya Senna Abdul Rachman, Arya. "3D-LIDAR Multi Object Tracking for Autonomous Driving: Multi-target Detection and Tracking under Urban Road Uncertainties." (2017).

[3] David Held, et al. "A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues." (2016).

(Optional) Future extensions / Unimplemented parts