CSCI435/CSCI935

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CSCI435/CSCI935
Computer Vision: Algorithms and Systems
Spring 2020
Assignment Three (25%)
Due Date: 5:00pm Sunday 8 November 2020
Objectives
 Design a C/C++ or Python program that extracts and counts moving objects, e.g. people, cars and
others using background modelling and segmentation of optical flow.
Introduction
Extraction of moving objects from a sequence of images or video is often used in many video analysis tasks.
For instance, it is a key component in intelligent video surveillance systems. In this assignment, you are
required to develop a program in C/C++ or Python using OpenCV 4.4.0 to detect, separate and count
moving objects from a given sequence of images or video captured by a stationary camera. There are three
tasks.
Task One (18%) – Background modelling
In this task, you are required to extract moving objects using background modelling. There are three key
steps involved in the extracting and counting moving objects:
1. Detecting moving pixels using background modelling and subtraction,
2. Removing noisy detection using morphological operators or majority voting and
3. Count separate moving objects using connected component analysis.
4. Classify each object (or connected component) into person, car and other by simply using the ratio of
width and height of the connected components.
OpenCV 4.4.0 provides various algorithms for each of the steps 1 & 2. However, you MAY have to
implement your own connected component analysis algorithm and classification algorithm. For simplicity,
you can assume that each connected component corresponds to one object.
Original Video Frame Estimated
Background Frame
Detected Moving
Pixels before
Filtering (in Binary
Mask)
Detected Objects
(in Original color)
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When running, the program should display the original video frames, estimated background frames, detected
moving pixels after the background modeling and subtraction (before any noise removal) and the detected
moving objects in a single window as illustrated above. The detected moving pixels before filtering should
be displayed in black and white (binary mask). The detected object has to be displayed in its original RGB
color (all background pixels should be displayed in black). At the same time, the number of objects or
connected components should be output to the command window as illustrated below:
Frame 0001: 0 objects
Frame 0002: 0 objects
…
Frame 0031: 5 objects (2 persons, 1 car and 2 others)
Frame 0032: 6 objects (3 persons, 1 cars and 2 others)
…
Frame 1000: 10 objects (
…
Task Three (7%) – Segmentation of moving objects
In this task, you are required to extract moving objects using segmentation of motion fields (e.g. optical
flows). You need to design your own solution by using the algorithms you have learned from the subject. The
solution must be described in the head of your source C/C++ or Python code.
Original Video
Frame One
Original Video
Frame Two
Estimated
Motion Field
Segmented
Objects
(In Original color)
The program should display the original video frames, estimated motion field, detected moving pixels and
the detected moving objects in a single window as illustrated above. The two original video frames are those
from which the motion field is estimated. The estimated motion field should be displayed using “arrow”
代写CSCI935编程设计、代做Python程序语言、C/C++编程实验调试
whose direction encodes motion direction and length encodes magnitude of the motion. Note that you may
choose to display motion flow of every other 2, 3 or 4 pixels so as not to clutter the display. The segmented
object has to be displayed in its original RGB color (all background pixels should be displayed in black). At
the same time, the number of objects or connected components should be output to the command window in
the same format as that of Task One.
Requirements on coding
1. The program should be named as “movingObj” and shall take an option, either –b or -s and a
video filename as the input, e.g. movingObj –b videofile or movingObj –s
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videofile. When –b is given, the program should perform Task One and when –s is given, the
program performs Task Two.
2. No other third-party libraries should be used in the program except OpenCV 4.4.0 or OpenCV
4.4.0-Python (assuming that numpy and matplotlib packages exist). The code has to be either in
C/C++ or Python.
3. Place your implementation in a single .cpp or .py file called movingObj.cpp or
countMovingObj.py
4. Description of the solution to Task Two should be given as comments at the beginning of the
movingObj.cpp or movingObj.py
5. No other third-party libraries should be used in the program except OpenCV 4.4.0. The code has to
be in either C/C++ or python-OpenCV 4.4.0.
Marking Scheme
Zero marks may be graded if your code cannot run or the code does not meet the requirements
Task One
1. Program structure, comments and usability (2%)
2. Read and display of the video frames (2%)
3. Background modeling or estimation (3%)
4. Subtraction and display of the detected moving pixels (3%)
5. Remove of noisy or false detection (2%)
6. Connected component analysis and display of the moving objects (2%)
7. Classification of moving objects (2%)
8. Output number of objects or connected components (2%)
Task Two
9. Description of the solution (1%)
10. Display of the two frames (1%)
11. Estimation and display of motion fields (3%)
12. Segmentation and display of the moving objects (2%)
Submission
Zip the following source and executable files to your_login_name.zip. The zip file must be submitted via
Moodle.
For C/C++ implementation: movingObj.cpp, movingObj.exe (Win10, openCV 4.4.0 MS Visual
C/C++ 2017 Release version, 64bits)
For Python implementation: movingObj.py (Win10, openCV 4.4.0 + Python 3.8.5)
IMPORTANT:
a) DO NOT include and submit any object files and videos in the zip file. Your submission may not be
accepted if you do so.
b) Submission through email WILL NOT be accepted如有需要，请加QQ：99515681 或邮箱：99515681@qq.com 微信：codehelp

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来源： https://www.cnblogs.com/greenjavat/p/13927668.html