Detection-and-Tracking-of-Cars-Vehicles-using-Computer-Vision

Steps involved:

1. Data evaluation

   • Analysis of data

   • Classification of car and not-car images

   • HOG feature extraction

   • Visualization of HOG features of images

2. HOG classifiers and SVC training

   • Extracting features from list of images

   • Train a linear support vector machine (SVM) classifier

3. Car/Vehicle detection pipeline

   • Predicting over feature extracted images and to draw bounding boxes

   • Sliding window technique is applied by varying parameters and detection is by trained classifiers

   • Heat map is genearted based on rectangular locations and boundng boxes apples on labels

   • Run a video through the pipeline

Datasets used:

GTI vehicle image database

KITTI vision benchmark suite

Caltech database

TU Graz database

Udacity labelled sets