Evidence map›Paper›PMID 32610450›Full record

ArticleSensors (Basel, Switzerland)2020

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors.

Yun Zhao, Xiang Zhou, Xing Xu, Zeyu Jiang, Fupeng Cheng, Jiahui Tang, Yuan Shen

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Yun ZhaoSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Xiang ZhouSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Xing XuSchool of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Zeyu JiangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Fupeng ChengSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Jiahui TangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Yuan ShenSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Zhejiang University of Science and Technology · CN

Funding

National Natural Science Foundation of China 61403346National Natural Science Foundation of China 61605173Natural Science Foundation of Zhejiang Province LY16C130003Science and technology project of Zhejiang Province 2019C54005
6 · The paper itself

Abstract

The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.

Indexed as

DDRID switchesocclusionvehicle trackingYOLO

Identifiers

PMID32610450
PMCPMC7374460
OpenAlexW3037468973

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.