This repository provides the implementation of our paper Tracking without bells and whistles (Philipp Bergmann, Tim Meinhardt, Laura Leal-Taixe) [https://arxiv.org/abs/1903.05625]. This branch includes an updated version of Tracktor for PyTorch 1.3 with an improved object detector. The original results of the paper were produced with the iccv_19
branch.
In addition to our supplementary document, we provide an illustrative web-video-collection. The collection includes exemplary Tracktor++ tracking results and multiple video examples to accompany our analysis of state-of-the-art tracking methods.
Clone and enter this repository:
git clone https://github.com/phil-bergmann/tracking_wo_bnw
cd tracking_wo_bnw
Install packages for Python 3.7 in virtualenv:
pip3 install -r requirements.txt
pip3 install -e .
MOTChallenge data:
Download model (MOT17 object detector, MOT20 object detector, and re-identification network) and MOTChallenge result files:
output
directory.In order to configure, organize, log and reproduce our computational experiments, we structured our code with the Sacred framework. For a detailed explanation of the Sacred interface please read its documentation.
Tracktor can be configured by changing the corresponding experiments/cfgs/tracktor.yaml
config file. The default configuration runs Tracktor++ with the FPN object detector as described in the paper.
The default configuration is Tracktor++
. Run Tracktor++
by executing:
python experiments/scripts/test_tracktor.py
The results are logged in the corresponding output
directory.
For reproducibility, we provide the new result metrics of this updated code base on the MOT17
challenge. It should be noted, that these surpass the original Tracktor results. This is due to the newly trained object detector. This version of Tracktor does not differ conceptually from the original ICCV 2019 version (see branch iccv_19
). The results on the official MOTChallenge webpage are denoted as the Tracktor++v2
tracker. The train and test results are:
********************* MOT17 TRAIN Results *********************
IDF1 IDP IDR| Rcll Prcn GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
65.2 83.8 53.3| 63.1 99.2 1638 550 714 374| 1732 124291 903 1258| 62.3 89.6 62.6
********************* MOT17 TEST Results *********************
IDF1 IDP IDR| Rcll Prcn GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
55.1 73.6 44.1| 58.3 97.4 2355 498 1026 831| 8866 235449 1987 3763| 56.3 78.8 56.7
We complement the results presented in the paper with MOT20
train and test sequence results. To this end, we run the same tracking pipeline as for MOT17
but apply an object detector model trained on the MOT20
training sequences. The corresponding model file is the same as used for this work.
********************* MOT20 TRAIN Results *********************
IDF1 IDP IDR| Rcll Prcn GT MT PT ML| FP FN IDs FM| MOTA
60.7 73.4 51.7| 68.5 97.4 2212 892 1064 259| 20860 357227 2664 6504| 66.4
********************* MOT20 TEST Results *********************
IDF1 IDP IDR| Rcll Prcn GT MT PT ML| FP FN IDs FM| MOTA
52.6 73.7 41.0| 54.3 97.6 1242 365 546 331| 6930 236680 1648 4374| 52.6
For the object detector, we followed the new native torchvision
implementations of Faster R-CNN with FPN which are pre-trained on COCO. The provided object detection model was trained and tested with the experiments/scripts/faster_rcnn_fpn_training.ipynb
Jupyter notebook. The object detection results on the MOT17Det
train and test sets are:
********************* MOT17Det TRAIN Results ***********
Average Precision: 0.9090
Rcll Prcn| FAR GT TP FP FN| MODA MODP
97.9 93.8| 0.81 66393 64989 4330 1404| 91.4 87.4
********************* MOT17Det TEST Results ***********
Average Precision: 0.8150
Rcll Prcn| FAR GT TP FP FN| MODA MODP
86.5 88.3| 2.23 114564 99132 13184 15432| 75.0 78.3
The training config file is located at experiments/cfgs/reid.yaml
.
Create reID data python experiments/evaluation_tools/reid_mot_to_coco_gt.py --dataset MOT17 --data_root data
Start training by executing python experiments/scripts/train_reid.py
.
If you use this software in your research, please cite our publication:
@InProceedings{tracktor_2019_ICCV,
author = {Bergmann, Philipp and Meinhardt, Tim and Leal{-}Taix{\'{e}}, Laura},
title = {Tracking without bells and whistles},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}}