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astgcn

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019

darknet

Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)

dcrnn

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

deep_sort

Simple Online Realtime Tracking with a Deep Association Metric

deep_sort_yolov3

Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow

gman

GMAN

graph-wavenet

graph wavenet

graphsage

Representation learning on large graphs using stochastic graph convolutions.

keras-yolo3

A Keras implementation of YOLOv3 (Tensorflow backend)

object-detection-and-tracking

YOLO & RCNN Object Detection and Multi-Object Tracking

real-time-detection-and-classification-of-vehicles-and-pedestrians-using-haar-cascade-classifier

Real Time Detection and Classification of Vehicles and Pedestrians Using Haar Cascade Classifier with Background Subtraction

speed-estimation-of-vehicles-with-plate-detection

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.

st-metanet

The codes and data of paper "Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning"

stgcn

implementation of STGCN for traffic prediction in IJCAI2018

stgcn_ijcai-18

Spatio-Temporal Graph Convolutional Networks

t-gcn

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

t-gcn-pytorch

tensorflow_object_counting_api

🚀 The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems!

test

traffic-speed-forecast

XGBRegressor and T-GCN for traffic speed prediction

vehicle-and-pedestrian-detection-using-haar-cascades

Real Time Detection and Classification of Vehicles and Pedestrians Using Haar Cascade Classifier

vehicle-and-speed-identification

Detecting Cars in real time and identifying the speed of cars and tracking

vehicle-speed-check

Vehicle Speed Check

vehicle_counting_and_classification

Tracks vehicles, classifies as moving up or down, estimates the speed of the vehicles within a boundary and finally predicts the type of the vehicle as light-, heavy-weight, or motor vehicle.

vehicle_counting_tensorflow

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

vehicle_detection

Using the OpenCV framework in Python to detect vehicles from a video stream, dataset trained with the help of Haar Cascade classifier.

yolo3-keras

这是一个yolo3-keras的源码，可以用于训练自己的模型。

yolo3-training-keras-master

YOLOv3 training with own data on GPU. A Keras implementation of YOLOv3

yolov3_deepsort

Object tracking implemented with YOLOv3, Deep Sort and Tensorflow.