🚘 "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).

Hobby project to track vehicles that are over speeding and violating red light

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

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

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

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

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

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

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

The name says everything...

We developed a system that leverages on YOLO Machine Learning Model for managing the traffic flow based on the vehicle density.

Car tracking and car counter implemented with YOLOX, ByteTrack and Pytorch.

vedai dataset for darknet

Implementation of the Automatic Emergency Braking System using deep learning.

A python project that does real-time vehicle detection using a trained car-cascade Model

A project to demonstrate how an adaptive LED high beam technology would work.

VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models