3D Multi-Object Tracker

This project is developed for tracking multiple objects in 3D scene. The visualization code is from here.

Features

• Fast: currently, the codes can achieve 700 FPS using only CPU (not include detection and data op), can perform tracking on all kitti val sequence in several seconds.
• Support both online and global implementation. The overall framework of design is shown below:

Kitti Results

Results on the Kitti tracking val seq [1,6,8,10,12,13,14,15,16,18,19] using second-iou and point-rcnn detections. We followed the HOTA metric, and tuned the parameters in this code by firstly considering the HOTA performance.

Prepare data

You can download the Kitti tracking pose data from here, and you can find the point-rcnn and second-iou detections from here.

To run this code, you should organize Kitti tracking dataset as below:

# Kitti Tracking Dataset       
└── kitti_tracking
       ├── testing 
       |      ├──calib
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      ├──image_02
       |      |    ├──0000
       |      |    ├──....
       |      |    └──0028
       |      ├──pose
       |      |    ├──0000
       |      |    |    └──pose.txt
       |      |    ├──....
       |      |    └──0028
       |      |         └──pose.txt
       |      ├──label_02
       |      |    ├──0000.txt
       |      |    ├──....txt
       |      |    └──0028.txt
       |      └──velodyne
       |           ├──0000
       |           ├──....
       |           └──0028      
       └── training # the structure is same as testing set
              ├──calib
              ├──image_02
              ├──pose
              ├──label_02
              └──velodyne 

Detections

└── point-rcnn
       ├── training
       |      ├──0000
       |      |    ├──000001.txt
       |      |    ├──....txt
       |      |    └──000153.txt
       |      ├──...
       |      └──0020
       └──testing 

Requirements

python3
numpy
opencv
yaml

Quick start

• Please modify the dataset path and detections path in the yaml file to your own path.
• Then run python3 kitti_3DMOT.py config/point_rcnn_mot.yaml
• The results are automatically saved to evaluation\results\sha_key\data, and evaluated by HOTA metrics.

Notes

The evaluation codes are copied from Kitti.