The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.

1. Real Time
Object & Pose Detection
GUIDED BY: GROUP MEMBERS:
Prof. Nisha Bhati Ashwin Bicholiya
Aishwary Joshi
Arjun Soni
Anshul Sharma

2. INTRODUCTION
• Efficient and accurate object detection has been an important topic
in the advancement of computer vision systems.
• Our project aims to detect the object with the goal of achieving
high accuracy with a real-time performance.
• In this project, we use a completely deep learning based approach
to solve the problem of object detection.
• The input to the system will be a real time image, and the output
will be a bounding box corresponding to all the objects in the
image, along with the class of object in each box.

3. OBJECTIVE
• Develop a application that detects an object and it can be used for
vehicles counting, when the object is a vehicle such as a bicycle or
car, it can count how many vehicles have passed from a particular
area or road and it can recognize human activity too.

4. Problem Domain
• Humans can easily detect and identify objects present in an image
but for the computer or machine a classifying and finding an
unknown number of individual objects within an image is extremely
a difficult problem.
• Although there exist object detection software and application
they do not give an accurate result of an object because despite a
lot of research, real-time, and dynamic object detection methods
are still in process.

5. Solution Domain
• To classify image objects and also to determine the objects
positions we use two different methods object classification and
localization in the same time.
• The true purpose of this project is that it can be used in security,
surveillance and autonomous vehicle driving to detect pedestrians
walking or jogging on the street to avoid accidents.
• We use object classification and localization it provides accuracy,
speed for real time detection and also improves detection tasks are
optimized using one multi-task function and an object is compared
to the image’s true objects.

6. Required Resources
• As the application is mobile based and will be available for Android devices.
Software Requirements:
 An integrated development environment (IDE) Microsoft Visual Studio code.
 Flutter (UI software development kit ).
 Dart programming language
 Tensorflow
 YOLO Framework.
Hardware Requirements:
 To run Visual Studio code we need-
• Operating system of Microsoft Windows 10 (32-or 64-bit)
• RAM- 4 GB
• Hard Disk- Minimum 1GB disk space

7. Methodology to be adopted

8. PREPROCESSING
• The process of preprocessing improves the image intensity by
suppressing the unwanted features or enhancing them for further
processing.
• It resizes the image size to 448*448 and also normalizes the
contrast and brightness effects. The image is also cropped and
resized so that feature extraction can be performed easily. The
input images are pre-processed and very easily normalize the
contrasts and brightness.

10. Yolo Framework
This model divides the image into S×S grid and for each grid cell predicts B
bounding boxes, confidence for those boxes, and C class probabilities.
This predictions are encoded as an S×S × (B*5+C) tensor.

11. Use-case Diagram
System
User
Initialize
Real time image
Classification
&Localization
Recognisation
General
Output
Upload Image
Open camera
Cancel
Image-Grid S×S
S×S Grid to
bound box
Capture image

12. Activity Diagram
Upload Image or Open
Camera
Image
format
not
supported
Format Accepted
Error found Pre Processing
Classification & Localization
Recognization
Output Generated

13. Sequence Diagram
USER GUI System
1. Display Menu
2. Open camera/upload image
3. Process Image
Localization
5. Classification
4. Preprocessing
7. General Output
8. Display Output
&

14. Input image
Architecture of the System
Pre Processing
Classification and
Localization
Output

15. What are Localization and detection
Classification
Classification with
Localization Detection
Multiple Objects

16. Classification with localization
Vector features
Multiple Layers
Outputs the
predicted class
1. pedestrian
2. Car
3. Motorcycle
4. Background
Bx, by, bh, bw
Bounding Boxes
0,0
1,1Bx, by
bh
bw

17. Defining the target label Y
1. pedestrian
2. Car
3. Motorcycle
4. Background
Need to output bx, by, bh, bw, class label(1-4)
X=
PC
Bx
By
Bh
Bw
C1
C2
C3
1
Bx
By
Bh
Bw
0
1
0
0
?
?
?
?
?
?
?
Y=Y=
Y=

18. Overlapping Objects:
PC
Bx
By
Bh
Bw
C1
C2
C3
PC
Bx
By
Bh
Bw
C1
C2
C3
PC
Bx
By
Bh
Bw
C1
C2
C3
Y=
Y=
Anchor box 1: Anchor box 2:
1
Bx
By
Bh
Bw
1
0
0
1
Bx
By
Bh
Bw
0
1
0
Y=