基于表示的回归算法在目标距离估计中的应用matlab代码.zip

Representation Based Regression for Object Distance Estimation ============================= This repository includes the implentation of the methods in [Representation Based Regression for Object Distance Estimation](https://www.sciencedirect.com/science/article/pii/S089360802200452X).  Software environment: ``` conda create -n distance conda activate distance conda install anaconda::tensorflow-gpu pip install opencv-contrib-python pip install pandas==1.0.4 pip install tqdm==4.46.1 pip install scikit-learn==0.24.1 ``` ``` 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. ``` ## Demo** (New) We have now included a demo using pretrained weights of ```CL-CSEN``` and ```CL-CSEN-1D``` methods. First, you need to download KITTI Dataset, refer to [Getting started with the KITTI Dataset](#Getting-started-with-the-KITTI-Dataset), then the demo can be run as follows, ``` cd demo/ python demo.py --method CL-CSEN ``` Press any key to proceed with the next object in the opened window or press ```q``` to exit. You can also change the method to ```--method CL-CSEN-1D```. Additionally, the feature extraction method can be changed by setting ```--feature_type``` to ```DenseNet121```, ```VGG19```, or ```ResNet50``` (DenseNet-121 [1], VGG19 [2], ResNet-50 [3]). For example, ``` python demo.py --method CL-CSEN --feature_type ResNet50 ``` ## Content: - [Citation](#Citation) - [Getting started with the KITTI Dataset](#Getting-started-with-the-KITTI-Dataset) - [Feature Extraction](#Feature-Extraction) - [Distance Estimation via Representation-based Classification](#Distance-Estimation-via-Representation-based-Classification) - [Sparse Representation-based Classification (SRC)](#Sparse-Representation-based-Classification-SRC) - [Collaborative Representation-based Classification (CRC)](#Collaborative-Representation-based-Classification-CRC) - [Distance Estimation using Representation-based Regression (RbR)](#Distance-Estimation-using-Representation-based-Regression-RbR) - [Convolutional Support Estimator Network (CSEN)](#Convolutional-Support-Estimator-Network-CSEN) - [Compressive Learning CSEN (CL-CSEN)](#Compressive-Learning-CSEN-CL-CSEN) - [Distance Estimation using Support Vector Regressor (SVR)](#Distance-Estimation-using-Support-Vector-Regressor-SVR) - [References](#References) ## Citation If you use method(s) provided in this repository, please cite the following paper: ``` @article{Ahishali, title = {Representation based regression for object distance estimation}, journal = {Neural Networks}, volume = {158}, pages = {15-29}, year = {2023}, issn = {0893-6080}, doi = {https://doi.org/10.1016/j.neunet.2022.11.011}, author = {Mete Ahishali and Mehmet Yamac and Serkan Kiranyaz and Moncef Gabbouj} } ``` ## Getting started with the KITTI Dataset The left color images of object dataset and the corresponding training labels can be obtained from [3D Object Detection Evaluation 2017](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). ``` 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 ``` ## Feature Extraction 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 ``` 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](#Sparse-Representation-based-Classification-SRC) and [CRC](#Collaborative-Representation-based-Classification-CRC). Note that the script of ```processFeatures.m``` produces the predicted distances using the CRC-light model that is discussed in the paper. ## Distance Estimation via Representation-based Classification 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. ### Sparse Representation-based Classification (SRC) 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```. ### Collaborative Representation-based Classification (CRC) 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. ## Distance Estimation using Representation-based Regression (RbR)  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 <em>Representation-based Regression (RbR)</em> for the proposed framework. ### Convolutional Support Estimator Network (CSEN) 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 ``` ### Compressive Learning CSEN (CL-CSEN) 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 ``` ## Distance Estimation using Support Vector Regressor (SVR) 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