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This repository contains code for the tracking system as described in ''Combined Image- and World-Space Tracking in Traffic Scenes'', ICRA 2017.

Home Page: https://www.vision.rwth-aachen.de/

CMake 0.37% C++ 98.42% C 1.20%
multi-object-tracking computer-vision tracking stereo-vision 3d-vision autonomous-vehicles autonomous-driving research-project research

ciwt's Introduction

Combined Image- and World-Space Tracking in Traffic Scenes

This repository contains code for the tracking system as described in Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017. (https://www.vision.rwth-aachen.de/media/papers/paper_final_compressed.pdf)

By Aljosa Osep, Wolfgang Mehner, Markus Mathias, Bastian Leibe at Computer Vision Group, RWTH Aachen University

Alt text

Demo Video

Click here to watch the video.

Prerequisite

In order to run the code, your setup has to meet the following minimum requirements (tested versions in parentheses. Other versions might work, too):

  • GCC 4.8.4
    • Eigen (3.x)
    • Boost (1.55 or later)
    • OpenCV (3.2.0 + OpenCV contrib)
    • PCL (1.8.x)

In case these are not installed on your system (eg. you have installed your libs to some weird directory such as /home/DOG/local) you need to set manually OpenCV_DIR, PCL_DIR, EIGEN_INCLUDE_DIRS by editing CMakeCache.txt.

Install

Compiling the code using CMake

  1. mkdir build
  2. cmake ..
  3. make all

Running the tracker

  1. Edit the config %PROJ_DIR%/data/kitti_sample.cfg, set all the paths.
  2. Run the tracker eg. CIWTApp --config %PROJ_DIR%/data/kitti_sample.cfg --start_frame 0 --end_frame 15 --show_visualization_2d --show_visualization_3d
  3. Find a small sample of KITTI tracking dataset in %PROJ_DIR%/data/kitti_sample (left/right camera images, Regionlets detections, calibration files).

Remarks

  • Tracking modes

    • There are two tracking modes, detection and detection_shape (set via --tracking_mode, or set in the config)
    • They perform similarly when evaluating MOTA in image-domain (KITTI eval. protocol), detection_shape provides significantly more precise localization in the 3D space while the detection mode is faster.
  • Data preprocessing

    • The tracker requires disparity maps to run, detection_shape additionally requires 3D segments (eg. generic object proposals, shipped with the tracker).
    • When you run the tracker for the first time, both will be computed on-the-fly, which will significantly slow-down the proc. time.
  • External libraries

  • Etc

    • The tracking framework does not ship a scene-flow estimator (you can get one here https://github.com/vogechri/PRSM)
    • In the paper experiments, we additionally used a scene-flow estimator to obtain velocity estimates of the 3D segments. You can input to the tracker velocity maps via --flow_map_path, but it is not necessary. Tracker will work just fine without it.
  • Run the tracker in release mode (oterwise it will be slow).

If you have any issues or questions about the code, please contact me https://www.vision.rwth-aachen.de/person/13/

Citing

If you find the tracker useful in your research, please consider citing:

@inproceedings{Osep17ICRA,
  title={Combined Image- and World-Space Tracking in Traffic Scenes},
  author={O\v{s}ep, Aljo\v{s}a and Mehner, Wolfgang and Mathias, Markus and Leibe, Bastian},
  booktitle={ICRA},
  year={2017}
}

License

GNU General Public License (http://www.gnu.org/licenses/gpl.html)

Copyright (c) 2017 Aljosa Osep Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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ciwt's Issues

Tracking mode detection_3DOP can not find PointCloud datasets

Hi, thanks for opensource this framework. I finally can run it on my computer, I want reproduce the result
in your paper which got the accurate 3D boundingbox. I assume should change mode to detection_3DOP mode. But once I set it in config, got this error:

Hello from CIWT!
Tracking mode: detection_3DOP.
Run tracker: YES.
[setPointCloudRenderingProperties] Could not find any PointCloud datasets with id <sample cloud>!

It can not find PointCloud datasets, but how should I set it, I have download the lidar data, how to set it? Or what data it need exactlly?

Which packages used from opencv_contrib

Hi, I am currently can not build all opencv_contrib depencies, how do you cmake opencv3.2? I try to build it but just got many unreasonable errors. Could you tell me what's package you used in ciwt, or let me know how to cmake the config. Many thanks Pal.

Could not load ground-plane parameters?

Hi,

When I running the code, there is a problem: Could not load ground-plane parameters, fitting plane of-the-fly ... Where can I find the ground-plane files? Or how to generate the ground-plane files just like the disparity map? I can't find the ground-plane files generate function in the code.

Thanks.

Segmentation Fault on Ubuntu 18.04

On Ubuntu 18.04, CIWTApp crashes with a segmentation fault. The output is:

Hello from CIWT!
Tracking mode: detection.
Run tracker: YES.
Loading KITTI detections for the whole sequence ...
Could not load ground-plane parameters, fitting plane of-the-fly ...
Segmentation fault (core dumped)

Running it in GDB produces the following backtrace:

Program received signal SIGSEGV, Segmentation fault.
__GI___libc_free (mem=0x21) at malloc.c:3103
3103	malloc.c: No such file or directory.
(gdb) bt
#0  __GI___libc_free (mem=0x21) at malloc.c:3103
#1  0x00007fffe7379332 in Eigen::internal::handmade_aligned_free (ptr=<optimized out>) at /usr/include/eigen3/Eigen/src/Core/util/Memory.h:98
#2  Eigen::internal::aligned_free (ptr=<optimized out>) at /usr/include/eigen3/Eigen/src/Core/util/Memory.h:179
#3  Eigen::internal::conditional_aligned_free<true> (ptr=<optimized out>) at /usr/include/eigen3/Eigen/src/Core/util/Memory.h:230
#4  Eigen::internal::conditional_aligned_delete_auto<float, true> (size=<optimized out>, ptr=<optimized out>)
    at /usr/include/eigen3/Eigen/src/Core/util/Memory.h:416
#5  Eigen::DenseStorage<float, -1, -1, 1, 0>::~DenseStorage (this=0x7fffffffb6c0, __in_chrg=<optimized out>)
    at /usr/include/eigen3/Eigen/src/Core/DenseStorage.h:542
#6  Eigen::PlainObjectBase<Eigen::Matrix<float, -1, 1, 0, -1, 1> >::~PlainObjectBase (this=0x7fffffffb6c0, __in_chrg=<optimized out>)
    at /usr/include/eigen3/Eigen/src/Core/PlainObjectBase.h:98
#7  Eigen::Matrix<float, -1, 1, 0, -1, 1>::~Matrix (this=0x7fffffffb6c0, __in_chrg=<optimized out>) at /usr/include/eigen3/Eigen/src/Core/Matrix.h:178
#8  pcl::RandomSampleConsensus<pcl::PointXYZRGBA>::computeModel (this=<optimized out>)
    at /home/sacusa/sd-apps/pcl-pcl-1.8.1/sample_consensus/include/pcl/sample_consensus/impl/ransac.hpp:62
#9  0x00007ffff3b825ef in pcl::SACSegmentation<pcl::PointXYZRGBA>::segment (this=0x7fffffffb870, inliers=..., model_coefficients=...)
    at /home/sacusa/sd-apps/pcl-pcl-1.8.1/segmentation/include/pcl/segmentation/impl/sac_segmentation.hpp:97
#10 0x0000000000b1a62d in SUN::utils::PlanarGroundModel::FitModel (this=0x25787c0, point_cloud_in=..., height_threshold=1.3999999999999999)
    at /home/sacusa/sd-apps/ciwt/src/sun_utils/ground_model.cpp:158
#11 0x0000000000b02d2f in SUN::utils::dirty::DatasetAssitantDirty::LoadData__KITTI (this=0x7fffffffd310, current_frame=0)
    at /home/sacusa/sd-apps/ciwt/src/sun_utils/datasets_dirty_utils.cpp:394
#12 0x0000000000b01c97 in SUN::utils::dirty::DatasetAssitantDirty::LoadData (this=0x7fffffffd310, current_frame=0, dataset_string="kitti")
    at /home/sacusa/sd-apps/ciwt/src/sun_utils/datasets_dirty_utils.cpp:263
#13 0x0000000000c37d49 in main (argc=7, argv=0x7fffffffde08) at /home/sacusa/sd-apps/ciwt/apps/CIWT.cpp:503

For reference, it was compiled with GCC 4.8.5 and the exact versions of the dependencies written in the README.

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