vehicle-detection

Udacity Self-Driving Car Engineer Nanodegree projects.

🚘 "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).

Vehicle Detection by Haar Cascades with OpenCV

Vehicle Detection, Tracking and Counting

Vehicle detection using YOLO in Keras runs at 21FPS

KITTI data processing and 3D CNN for Vehicle Detection

This is a Matlab lesson design for vehicle detection and recognition. Using cifar-10Net to training a RCNN, and finetune AlexNet to classify. Thanks to Cars Dataset:http://ai.stanford.edu/~jkrause/cars/car_dataset.html

OpenCV implementation of lane and vehicle tracking

Vehicle Detection with Convolutional Neural Network

The code of the Object Counting API, implemented with the YOLO algorithm and with the SORT algorithm

real-time Vehicle Detection( tiny YOLO ver) and HOG+SVM method

Vehicle detection, tracking and counting by blob detection with OpenCV on c++.

Official version of 'Weakly Supervised 3D object detection from Lidar Point Cloud'(ECCV2020)

Vehicle detection, tracking and counting by SVM is trained with HOG features using OpenCV on c++.

Detect vehicles in a video

According to YOLOv3 and SORT algorithms, counting multi-type vehicles. Implemented by Pytorch.

This project aims to count every vehicle (motorcycle, bus, car, cycle, truck, train) detected in the input video using YOLOv3 object-detection algorithm.

This repository contains works on a computer vision software pipeline built on top of Python to identify Lanes and vehicles in a video. This project is not part of Udacity SDCND but is based on other free courses and challanges provided by Udacity. It uses Computer vision and Deep Learrning Techniques. Few pipelines have been tried on SeDriCa, IIT Bombay.

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

Detect and track vehicles in video

Driving risk assessment with deep learning using a monocular camera. Related paper: https://arxiv.org/abs/1906.02859

Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.

This is one of the best vehicle recognition applications. It can determine the car's license plate number, color, model, brand and year.

Sample use cases in OpenCV 🎨

Automatic detection and tracking of moving vehicles in a video from a surveillance camera

Collection of automated tasks

A Network for detecting and classifying vehicle's front and rear

vedai dataset for darknet