Small Object Detection Base on YOLOv3 for Pedestrian Recognition

Project Code :TMMAAI54

Objective

Object detection is the most important algorithm in pattern recognition. This process helps us to find the count of persons in the video, track the persons using deep learning techniques.

Abstract

In this work we will detect pedestrians on road using Deep Learning Techniques. Both detection and tracking people are challenging problems, especially in complex real-world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scenes, but false positives have remained frequent. 

The identification of particular individuals has remained challenging as well. The approximate articulation of each person is detected using YOLOv2 pretrained detector network. The Dataset used here is collected from Kaggle pedestrian’s dataset.

 Keywords: Detection, Convolution Neural network, Deep Learning, YOLOV2.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software & Hardware Requirements:

Software: Matlab 2018a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support            

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    • Morphological processing
    • Segmentation etc.,
  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will be able to know what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:
    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network
  • How to detect the object using AI
  • How to extend our work to another real time applications
  • Project development Skills:
    • Problem analyzing skills
    • Problem-solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

Demo Video

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