This repository includes a modified implentation of the methods in Representation Based Regression for Object Distance Estimation. Our group used the Regressor Model to create a distance estimator for inter-object distances.
Modification authors: Benedikt Schröter, Nils Möbus, Carla Frenzel, Tim Rosenkranz - Goethe University Frankfurt am Main, Germany
Original Readme:
Software environment:
1. Python with the following version and libraries.
python == 3.7.7
tensorflow == 2.1.0
Numpy == 1.18.5
Matplotlib == 3.2.2
SciPy == 1.5.0
scikit-learn == 0.24.1
argparse == 1.1
OpenCV == 4.2.0
pandas == 1.0.4
tqdm == 4.46.1
2. MATLAB -> MATLAB R2019a.
Content:
- Citation
- Getting started with the KITTI Dataset
- Feature Extraction
- Distance Estimation via Representation-based Classification
- Distance Estimation using Representation-based Regression (RbR)
- Distance Estimation using Support Vector Regressor (SVR)
- References
If you use method(s) provided in this repository, please cite the following paper:
@misc{ahishali2021representation,
title={Representation Based Regression for Object Distance Estimation},
author={Mete Ahishali and Mehmet Yamac and Serkan Kiranyaz and Moncef Gabbouj},
year={2021},
eprint={2106.14208},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
The left color images of object dataset and the corresponding training labels can be obtained from 3D Object Detection Evaluation 2017.
unzip data_object_image_2.zip
unzip data_object_label_2.zip
After unzipping, move them under kitti-data/ folder and using generate-csv.py script generate a csv file for the KITTI annotations:
python kitti-data/generate-csv.py \
--input=kitti-data/training/label_2/ \
--output=kitti-data/annotations.csv
Further, for our modified model it is necessary to run the scripts give-ids.py and generate-data-interdist.py
Features are extracted using feature_extraction.py script. The supported models are DenseNet121, VGG19, and ResNet50 (DenseNet-121 [1], VGG19 [2], ResNet-50 [3]):
python feature_extraction.py --model VGG19 [-s <sample size>]
We added the optional argument for a sample size. The default value is 20,000 and we advise to not go below that value. The maximum number of samples is ~140,000.
Next, the features are further processed and ordered using processFeatures.m. In the script, please also set the proper model name param.modelName to either DenseNet121, VGG19, or ResNet50 and param.DicDesign to 2D or 1D corresponding to the dictionary designs used in CSEN and CL-CSEN approaches. This procedure is only needed for the CSEN, CL-CSEN, and SVR approaches. If you are only interested in running SRC and CRC methods, you may proceed to the related sections: SRC and CRC. Note that the script of processFeatures.m produces the predicted distances using the CRC-light model that is discussed in the paper.
We formulate the distance estimation task as a representation-based classification problem by estimating the quantized distance values. For example, for the objects between [0.5, 60.5] meters away from the camera, we estimate a quantized distance level from 60 different distance levels ranging between [1, 60] with 1-meter senstivity.
There are implemented 8 different SRC algorithms for the distance estimation task including ADMM [4], Dalm [5], OMP [5], Homotopy [6], GPSR [7], L1LS [8], ℓ1-magic [9], and Palm [5]. You may run all of them in once as follows:
cd src/
run main.m
Alternatively or preferably (e.g., you may choose a specific SRC method since the required run-time is huge for running all SRC methods in once), the selected ones can be defined in the script:
l1method={'solve_ADMM','solve_dalm','solve_OMP','solve_homotopy','solveGPSR_BCm', 'solve_L1LS','solve_l1magic','solve_PALM'}; %'solve_PALM' is very slow
Similarly, please also set the proper model name param.modelName to either DenseNet121, VGG19, or ResNet50.
Distance estimation using the CRC method [10] can be run as follows:
cd crc\
run main.m
The CRC-light model can be run by setting CRC_light = 1 in the script. Please change the model name param.modelName to DenseNet121, VGG19, or ResNet50 to try different features.
Contrary to previous methos, it is possible to directly estimate the object distance information without the quantization step using CSEN and CL-CSEN approaches. As CSEN and CL-CSEN approaches still utilize the representative dictionary, we introduce the term Representation-based Regression (RbR) for the proposed framework.
The CSEN implementation is run as follows:
python regressor_main.py --method CSEN --feature_type DenseNet121
Note that similarly, the feature type can be set to DenseNet121, VGG19, or ResNet50. If you like, only testing can be performed using the provided weights:
python regressor_main.py --method CSEN --feature_type DenseNet121 --weights True
The CL-CSEN implementation is run as follows:
python regressor_main.py --method CL-CSEN --feature_type DenseNet121
The parameter --feature_type can be set to DenseNet121, VGG19, or ResNet50.
Testing of CL-CSEN with the provided weights:
python regressor_main.py --method CL-CSEN --feature_type DenseNet121 --weights True
The SVR method is implemented as a competing regressor. We use the Nystroem method for the kernel approximation since it is unfeasible to compute exact kernel mapping with the given high-dimensional dataset. Hyperparameter search is applied with grid search and then the performance is computed with the found optimal SVR parameters:
python regressor_main.py --method SVR --feature_type DenseNet121
The parameter --feature_type can be set to DenseNet121, VGG19, or ResNet50.
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