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ECSE-415 Introduction to Computer Vision
创建日期：2024-04-20 17:06:04
标签： ECSE-415
Introduction to Computer Vision

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ECSE-415 Introduction to Computer Vision

Final Project: Vehicle Counting
1 Introduction
The goal of this project is to create algorithms for extremely overlapping vehi-
cle counting in traffic congestion situations. We will mainly take two different
approaches for vehicle counting (1) Counting using detection (2) Direct vehicle
count regression from images. The dataset used in this project is TRaffic ANd
COngestionS (TRANCOS) dataset [1]. It consists of 1244 images, with a total
of 46796 vehicles. Each image is of size 640x480 pixels. TRANCOS dataset is
divided into three subsets: train (623 images), valid (200 images), and test (421
images). CSV files for all three subsets are given with the project 1. The CSV
files provide the name of the images and the corresponding ground-truth vehicle
count for that image. You should use training and validation data to create your
final model, and then use this final model to present your predictions on test
data. This predictions should be uploaded to class’s Kaggle competition web
page. Note that your team is entitled to two submissions per day. you can learn
more about how to submit your results here. Note that you will need to create
an account on the Kaggle site. Each group member should make an account,
and then merge with the other members to form a single team. Submissions
should only be done by a single team rather by each group member.
Students should read the TRANCOS paper to learn about different ap-
proaches to vehicle counting. An example of the images in this dataset is shown
in the figure below.
For this project, you will form a team of 3 or 4 people. The objective of
the project is to permit your team to explore different approaches for vehicle
counting.
Projects should be submitted electronically via myCourses. All submitted ma-
terial is expected to be the students’ own work or appropriately cited (See
Academic Integrity guidelines in the Syllabus). Software packages (e.g. scikit-
learn, MATLAB toolboxes, Keras, PyTorch, etc.) which implement machine
1Check Appendix-A for python code snippet to read the CSV file.
1
learning-related algorithms (SVM, RF, k-fold cross-validation, etc.) are allowed.
Appropriate citations are expected. The use of external code without citation
will be treated as plagiarism. Projects received after the deadline up to 3 days
late will be penalized by 10% per day. Projects received after Monday Decem-
ber 13 will not be graded. The project will be graded out of a total 100 points.
2 Submission Instructions
1. Submit (i) report in pdf format, (ii) all code (together in a single zip file)
2. Comment your code appropriately.
3. Make sure that the submitted code is running without error. Add a README
file if required.
4. The report should be comprehensive but concise.
5. If external libraries were used in your code, please specify them by name
and version in the requirement.txt file.
6. Submissions that do not follow the format will be penalized 10%.
Figure 1: TRANCOS dataset example
2
3 Vehicle Counting
There are three parts to the project:
1. Choose an existing vehicle detection technique, such as YOLO, Faster-
RCNN, where code is already available (e.g. you can find useful code on
the Facebook Detectron2 site, Vehicle Detection with YOLO). Use this to
obtain bounding boxes around vehicle in the traffic images. Then, write
python code to eliminate duplicate detections (if any) and to count the
bounding boxes.
2. Choose any new deep convolutional neural network (CNN) architecture
(e.g., ResNet [2], VGG [3], etc.) with a single neuron output layer, and
use an L2 loss (ridge regression) for training the CNN on the training set.
You can use validation set to tune the hyperparameters of the network.
Note that you can either train the network from the scratch or you can
use a pre-trained network and fine-tune it on the TRANCOS dataset.
3. Implement your own vehicle detection algorithm, based on a Support Vec-
tor Machine or Random Forest classifier (such as that used in Assignment
3). For this part of the project, you need to implement only one of the
following two approaches.
(a) Get the positive examples by cropping out image patches containing
vehicles from the TRANCOS dataset. Negative examples can be ob-
tained by extracting randomly located patches from the images (that
do not intersect vehicle patches). You can use the vehicle detector
implemented in part 1 to identify the vehicle patches in each image.
From these patches you should compute suitable features to feed into
the SVM or the RF. Typically the raw intensity values will not work
too well. You could try HoG or Haar features. You could also try Lo-
cal Binary Pattern (LBP) features. These were not covered in class,
but are fairly simple and fast. Train your SVM or RF using the ex-
tracted positive and negative examples. It is up to you to determine
how many examples you need to do the training. Once you have the
SVM-based vehicle detector working, you can use it in place of the
pre-made detector in part 1, to make a new version of the complete
vehicle counting system.
(b) Train a Support Vector Regressor or Random Forest Regressor us-
ing appropriate features to directly predict the number of vehicles
in an image. Typically the raw intensity values will not work too
well. You could try HoG or Haar features. You could also try Local
Binary Pattern (LBP) features. These were not covered in class, but
are fairly simple and fast. You can refer to the original TRANCOS
dataset paper for choosing appropriate features.
To evaluate the vehicle counting, we will use Mean Deviation Error (MDE)
Metric. MDE is calculated as a weighted Mean Absolute Error, where weights
3
are defined as 1/real vehicle count for each image. Calculate these metrics for
each image in the validation dataset for all three approaches above. Calculate
the same for the test dataset using the Kaggle competition.
In addition to this (for all three approaches), also display the scatter plot
of true vehicle count (X-axis) vs the Predicted Vehicle Count (Y-Axis). Report
the pearson correlation coefficient on the validation set. Write your observations.
At last, upload your best results on vehicle counts in test data to Kaggle.
Please refer to Kaggle competition web page to understand how your submission
should look like.
The final report should include the performance of the vehicle counting as
expressed above. The grade for the project will be based on the following items:
• Demo - the grader will check and run the provided code to verify that it
(more-or-less) works properly.
• Report - the grader will look for the required report elements: description
of algorithms used (with reference to code), reasoning behind the design
choices (used features), evaluation (using MSE, MAE, Pearson Correlation
metrics and the scatter plot), discussion of issues arising from the design
(e.g. difficulties encountered, effectiveness of different types of features,
etc). Mention your Kaggle team at the starting of the report.
The project must be done in groups of 3 or 4 students. Each student must
do a significant part of the project, and the role of each student should be stated
in the report. All group members will receive the same grade.
Appendix-A

案例分类
标签云
关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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