Smart IoT system for managing people congestion in delimited spaces.
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Updated
Jan 24, 2020 - Python
Smart IoT system for managing people congestion in delimited spaces.
This repository provides the implementation of our paper "Exploring the Potential of Synthetic Data for Pedestrian Analysis" delivered for the "Computer Vision and Cognitive System" course @Unimore
deployableFiles from the simulation
Pre-print version of "Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations"
This repository contains a real-time pedestrian detection and tracking system implemented using deep learning techniques, specifically leveraging the YOLO (You Only Look Once) V8 architecture. The system is designed to detect and track pedestrians in low-light conditions, making it suitable for applications such as night-time driving scenarios.
An AI based system that do detects the cars and the pedestrians from a captured video.
Results from 2014 Newport pedestrian safety survey conducted by the Bicycle and Pedestrian Advisory Commission
Haar cascade for detecting vehicles and pedestrians in videos using Python
Матричное моделирование пассажиропотоков для оптимизации планировки здания.
safety-data - react client
A Lightweight Residual Graph CNN for Pedestrians Trajectory Prediction
Analysis of when and where New York City (NYC) vehicle collisions occur with a focus on collisions involving pedestrians and cyclists.
A geospatial dataset focusing on walkability and pedestrian access
Yet Another attempt to build a traffic system in Unity.
AI car & pedestrian tracking in python utilizing computer vision
The Arrogance of Space Mapping Tool
This project uses Histogram of Oriented Gradients for pedestrian detection and Kalman Filter for tracking and prediction
MonoLoco++ and MonStereo for 3D localization, orientation, bounding box dimensions and social distancing from monocular and / or stereo images. PyTorch Official Implementation.
Hungarian algorithm + Kalman filter multitarget (multi-object) tracker implementation.
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