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Vehicle Tracking Using Deep SORT with Low Confidence Track Filtering

https://doi.org/10.1109/avss.2019.8909903
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-68279-0_2openalex-snapshot
The Hungarian Method for the Assignment Problem
inferredcites →https://doi.org/10.1016/j.cviu.2020.102907openalex-snapshot
UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking
inferredcites →https://doi.org/10.1109/avss.2017.8078516openalex-snapshot
High-Speed tracking-by-detection without using image information
inferredcites →https://doi.org/10.1109/avss.2018.8639144openalex-snapshot
Extending IOU Based Multi-Object Tracking by Visual Information
inferredcites →https://doi.org/10.1109/avss.2018.8639321openalex-snapshot
Multi-object Tracking Cascade with Multi-Step Data Association and Occlusion Handling
inferredcites →https://doi.org/10.1109/cvpr.2011.5995604openalex-snapshot
Globally-optimal greedy algorithms for tracking a variable number of objects
inferredcites →https://doi.org/10.1109/cvpr.2012.6247893openalex-snapshot
Discrete-continuous optimization for multi-target tracking
inferredcites →https://doi.org/10.1109/cvpr.2014.159openalex-snapshot
Robust Online Multi-object Tracking Based on Tracklet Confidence and Online Discriminative Appearance Learning
inferredcites →https://doi.org/10.1109/cvpr.2014.167openalex-snapshot
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
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/iccv.2013.286openalex-snapshot
The Way They Move: Tracking Multiple Targets with Similar Appearance
inferredcites →https://doi.org/10.1109/iccv.2015.133openalex-snapshot
Scalable Person Re-identification: A Benchmark
inferredcites →https://doi.org/10.1109/iccv.2015.384openalex-snapshot
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot
Mask R-CNN
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/icme.2017.8019461openalex-snapshot
Evolving boxes for fast vehicle detection
inferredcites →https://doi.org/10.1109/icme.2018.8486597openalex-snapshot
Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot
Object Detection with Discriminatively Trained Part-Based Models
inferredcites →https://doi.org/10.1109/wacv.2018.00087openalex-snapshot
Deep Cosine Metric Learning for Person Re-identification

What points at this work

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

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RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort
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RETRACTED: Vision-based tracking method of nighttime construction workers by integrating YOLOv5 and Deepsort (REDIRECT)
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