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Found 8 papers, 0 papers with code
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Generally, crowd datasets can be collected or generated from real or synthetic sources.
CrowdSim2: an Open Synthetic Benchmark for Object Detectors
Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision.
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting
Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks.
Deep Learning Techniques for Visual Counting
In this dissertation, we investigated and enhanced Deep Learning (DL) techniques for counting objects, like pedestrians, cells or vehicles, in still images or video frames.
MOBDrone: a Drone Video Dataset for Man OverBoard Rescue
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i. e., man overboard (MOB).
Multi-Camera Vehicle Counting Using Edge-AI
This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras.
Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation
Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens.
Virtual to Real adaptation of Pedestrian Detectors
Furthermore, we demonstrate that with our Domain Adaptation techniques, we can reduce the Synthetic2Real Domain Shift, making closer the two domains and obtaining a performance improvement when testing the network over the real-world images.
