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activiti icon activiti

Activiti is a light-weight workflow and Business Process Management (BPM) Platform targeted at business people, developers and system admins. Its core is a super-fast and rock-solid BPMN 2 process engine for Java. It's open-source and distributed under the Apache license. Activiti runs in any Java application, on a server, on a cluster or in the cloud. It integrates perfectly with Spring, it is extremely lightweight and based on simple concepts.

adaboost icon adaboost

Adaboost implementation using bayesian and decision tree classifiers

barefoot icon barefoot

Java library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloud.

fmm icon fmm

Fast map matching, a high performance algorithm

graphhopper icon graphhopper

An open source route planning library and server using OpenStreetMap.

ivmm icon ivmm

An Interactive-Voting Based Map Matching Algorithm

jackson-datatype-hibernate icon jackson-datatype-hibernate

Add-on module for Jackson JSON processor which handles Hibernate (http://www.hibernate.org/) datatypes; and specifically aspects of lazy-loading

kalman-and-bayesian-filters-in-python icon kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

map-matching icon map-matching

Map Matching based on GraphHopper i.e. 'snap' GPX records to the digital road network

map_matching-1 icon map_matching-1

Algorithms to find the streets that vehicle should have traveled to generate a given GPS track

opencv icon opencv

Open Source Computer Vision Library

pytorch-captcha-recognition icon pytorch-captcha-recognition

基于CNN训练的一套 "端到端" 的验证码识别模型,使用深度学习+训练数据+大量计算力,纯数字识别率高达 99.99%,数字+字母识别率 96%

rnn-trajmodel icon rnn-trajmodel

The source of the IJCAI2017 paper "Modeling Trajectory with Recurrent Neural Networks"

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