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

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

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

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…

Vehicle Detection with Convolutional Neural Network

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

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

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

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

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.