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Detecting, Tracking and Counting People Getting On/Off a Metropolitan Train Using a Standard Video Camera

https://doi.org/10.3390/s20216251
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Microsoft COCO: Common Objects in Context
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Ten Years of Pedestrian Detection, What Have We Learned?
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A Graph Based People Silhouette Segmentation Using Combined Probabilities Extracted from Appearance, Shape Template Prior, and Color Distributions
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The Urban Transport Crisis in Emerging Economies: A Comparative Overview
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Re-identification of Pedestrians in Crowds Using Dynamic Time Warping
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A Critical View of Context
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot
The Pascal Visual Object Classes (VOC) Challenge
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Multiple object tracking: A literature review
inferredcites →https://doi.org/10.1016/j.ijleo.2013.03.057openalex-snapshot
A novel approach for crowd video monitoring of subway platforms
inferredcites →https://doi.org/10.1016/j.ijleo.2019.163675openalex-snapshot
Passenger flow counting in buses based on deep learning using surveillance video
inferredcites →https://doi.org/10.1016/j.ins.2020.05.009openalex-snapshot
A fast occluded passenger detector based on MetroNet and Tiny MetroNet
inferredcites →https://doi.org/10.1016/j.neucom.2018.01.092openalex-snapshot
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey
inferredcites →https://doi.org/10.1016/j.neucom.2019.11.023openalex-snapshot
Deep learning in video multi-object tracking: A survey
inferredcites →https://doi.org/10.1016/j.patcog.2017.11.007openalex-snapshot
Deep visual tracking: Review and experimental comparison
inferredcites →https://doi.org/10.1016/j.patrec.2011.01.004openalex-snapshot
Face recognition using Histograms of Oriented Gradients
inferredcites →https://doi.org/10.1016/j.procs.2012.09.127openalex-snapshot
Upper Body Tracking Using KLT and Kalman Filter
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Challenges and opportunities in developing urban modal shift
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Measuring the potential emission reductions from a shift towards public transport
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Tiny YOLO Optimization Oriented Bus Passenger Object Detection
inferredcites →https://doi.org/10.1049/cp.2018.1292openalex-snapshot
Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
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Simulating Performance Impacts of Bus Lanes and Supporting Measures
inferredcites →https://doi.org/10.1080/12265934.2019.1577747openalex-snapshot
Impediments to the bus rapid transit implementation in developing countries – a typical evidence from Hanoi
inferredcites →https://doi.org/10.1088/1757-899x/688/4/044025openalex-snapshot
Metro Passenger Flow Statistics Based on YOLOv3
inferredcites →https://doi.org/10.1108/wjstsd-06-2015-0033openalex-snapshot
Study on the separated bus lanes effectiveness in city centres
inferredcites →https://doi.org/10.1109/avss.2019.8909903openalex-snapshot
Vehicle Tracking Using Deep SORT with Low Confidence Track Filtering
inferredcites →https://doi.org/10.1109/cvpr.1999.784637openalex-snapshot
Adaptive background mixture models for real-time tracking
inferredcites →https://doi.org/10.1109/cvpr.2001.990540openalex-snapshot
Virtual sample generation for template-based shape matching
inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2006.119openalex-snapshot
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
inferredcites →https://doi.org/10.1109/cvpr.2011.5995384openalex-snapshot
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inferredcites →https://doi.org/10.1109/cvpr.2013.426openalex-snapshot
Local Fisher Discriminant Analysis for Pedestrian Re-identification
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2019.00734openalex-snapshot
Few-Shot Adaptive Faster R-CNN
inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot
Fast R-CNN
inferredcites →https://doi.org/10.1109/iccv.2015.534openalex-snapshot
Learning to Track: Online Multi-object Tracking by Decision Making
inferredcites →https://doi.org/10.1109/iccv.2019.00667openalex-snapshot
CenterNet: Keypoint Triplets for Object Detection
inferredcites →https://doi.org/10.1109/icip.2016.7533003openalex-snapshot
Simple online and realtime tracking
inferredcites →https://doi.org/10.1109/icip.2017.8296962openalex-snapshot
Simple online and realtime tracking with a deep association metric
inferredcites →https://doi.org/10.1109/icpr.2000.902888openalex-snapshot
Tools and techniques for video performance evaluation
inferredcites →https://doi.org/10.1109/icpr.2004.1333992openalex-snapshot
Improved adaptive Gaussian mixture model for background subtraction
inferredcites →https://doi.org/10.1109/icpr.2008.4761705openalex-snapshot
Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection
inferredcites →https://doi.org/10.1109/pervasive.2015.7087180openalex-snapshot
Image processing techniques for object tracking in video surveillance- A survey
inferredcites →https://doi.org/10.1109/synasc.2018.00041openalex-snapshot
Optimizing the Trade-Off between Single-Stage and Two-Stage Deep Object Detectors using Image Difficulty Prediction
inferredcites →https://doi.org/10.1109/tits.2008.922943openalex-snapshot
Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models
inferredcites →https://doi.org/10.1109/tits.2020.2997084openalex-snapshot
Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review
inferredcites →https://doi.org/10.1109/tpami.2011.239openalex-snapshot
Tracking-Learning-Detection
inferredcites →https://doi.org/10.1109/tpami.2014.2345390openalex-snapshot
High-Speed Tracking with Kernelized Correlation Filters
inferredcites →https://doi.org/10.1109/tpami.2015.2509974openalex-snapshot
Struck: Structured Output Tracking with Kernels
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/uvs.2019.8658300openalex-snapshot
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
inferredcites →https://doi.org/10.1117/1.jei.25.5.051206openalex-snapshot
Vision-based traffic surveys in urban environments
inferredcites →https://doi.org/10.1145/1177352.1177355openalex-snapshot
Object tracking
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2008/246309openalex-snapshot
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
inferredcites →https://doi.org/10.1186/s12544-020-00405-xopenalex-snapshot
Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data
inferredcites →https://doi.org/10.1587/transinf.e93.d.1761openalex-snapshot
People Detection and Re-Identification in Complex Environments
inferredcites →https://doi.org/10.20858/tp.2018.13.2.1openalex-snapshot
SPEED MANAGEMENT EFFICACY ON NATIONAL ROADS - EARLY EXPERIENCES OF SECTIONAL SPEED SYSTEM FUNCTIONING IN PODLASKIE VOIVODSHIP
inferredcites →https://doi.org/10.3141/2143-08openalex-snapshot
Influence of Platform Height, Door Width, and Fare Collection on Bus Dwell Time
inferredcites →https://doi.org/10.3390/rs11050531openalex-snapshot
Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery
inferredcites →https://doi.org/10.48550/arxiv.2004.10934openalex-snapshot
YOLOv4: Optimal Speed and Accuracy of Object Detection
inferredcites →https://doi.org/10.5038/2375-0901.16.4.2openalex-snapshot
Shared-Use Bus Priority Lanes on City Streets: Approaches to Access and Enforcement

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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