Udacity Self-Driving Car Engineer Nanodegree projects.
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Updated
Mar 28, 2023 - C++
Udacity Self-Driving Car Engineer Nanodegree projects.
Vehicle Detection by Haar Cascades with OpenCV
🚘 "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.
Vehicle Detection, Tracking and Counting
Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
Vehicle detection using YOLO in Keras runs at 21FPS
KITTI data processing and 3D CNN for Vehicle Detection
OpenCV implementation of lane and vehicle tracking
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
Vehicle Detection with Convolutional Neural Network
Sample use cases in OpenCV 🎨
Vehicle detection, tracking and counting by blob detection with OpenCV on c++.
real-time Vehicle Detection( tiny YOLO ver) and HOG+SVM method
The code of the Object Counting API, implemented with the YOLO algorithm and with the SORT algorithm
This project aims to count every vehicle (motorcycle, bus, car, cycle, truck, train) detected in the input video using YOLOv3 object-detection algorithm.
A python project that does real-time vehicle detection using a trained car-cascade Model
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 followe…
Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.
According to YOLOv3 and SORT algorithms, counting multi-type vehicles. Implemented by Pytorch.
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