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Human detection and Tracking

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Introduction

In this project we have worked on the problem of human detection,face detection, face recognition and tracking an individual. Our project is capable of detecting a human and its face in a given video and storing Local Binary Pattern Histogram (LBPH) features of the detected faces. LBPH features are the key points extracted from an image which is used to recognize and categorize images. Once a human is detected in video, we have tracked that person assigning him a label. We have used the stored LBPH features of individuals to recognize them in any other videos. After scanning through various videos our program gives output like- person labeled as subject1 is seen in video taken by camera1, subject1 is seen in video by camera2. In this way we have tracked an individual by recognizing him/her in the video taken by multiple cameras. Our whole work is based on the application of machine learning and image processing with the help of openCV.This code is built on opencv 3.1.1, python 3.4 and C++, other versions of opencv are NOT SUPPORTED.

Requirements

Approach

Installation

Python

Don't forget to install the necessary libraries described in the install paragraph above.

First you need to run the create_face_model.py file, which uses the images in /data to create a .yaml file

python create_face_model.py
python3 main.py -v /path/to/input/videos/  

Example- for our directory structure it is:

 python3 main.py -v /video 

C++

 g++ -ggdb `pkg-config --cflags opencv` -o `basename name_of_file.cpp .cpp` name_of_file.cpp `pkg-config --libs opencv` 

Example- for our directory structure it is:

 g++ -ggdb `pkg-config --cflags opencv` -o `basename main.cpp .cpp` main.cpp `pkg-config --libs opencv` 
./name_of_file /path/to/input/video_file 

Example- for our directory structure it is:

 ./main /video/2.mp4

Performance of code

Results

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You can find project report here

To do

Special Thanks to: