Infrared Image Pedestrian Detection via YOLO-V3

Project Code :TCMAPY575

Objective

Infrared Image Pedestrian Detection is one of the challenging application of computer vision, which has been widely applied in many areas for e.g. autonomous cars, Robotics, Security tracking, Guiding Visually Impaired Peoples etc.

Abstract

Infrared Image Pedestrian Detection is one of the challenging application of computer vision, which has been widely applied in many areas for e.g. autonomous cars, Robotics, Security tracking, Guiding Visually Impaired Peoples etc. With the rapid development of deep learning many algorithms were improving the relationship between video analysis and image understanding. All these algorithms work differently with their network architecture but with the same aim of detecting multiple persons within complex image. Absence of vision impairment restraint the movement of the person in an unfamiliar place and hence it is very essential to take help from our technologies and trained them to guide blind peoples whenever they need.

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

Block Diagram

Specifications

Operating system                    :  Windows 7 or 7+

Ram                                         :  8 GB

Hard disc or SSD                    :  More than 500 GB

Processor                                 :  Intel 3rd generation or high or Ryzen with 8 GB Ram

Software’s                               :  Python 3.6 or high version, Visual studio, PyCharm.

Learning Outcomes

 ·       Yolov3

·        Django

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