Vehicle detection and tracking using kalman filter and hungarian algorithm for driving assistance in VANET’s

Abstract

In today’s world, the huge increase in automobile vehicles counts on the roads in both rural and urban areas have turned out to create large number of issues which result in the administering and governing of the vehicle on the roads and highways for better driving assistance. Vehicle identification and monitoring by using the information collected from transport monitoring system leads to a defining standard approach for the complex transportation system. In our proposed research Kalman filtering and Hungarian approach used for the identification and tracking of vehicles which mainly focuses on moving vehicles in context of vehicular ad-hoc networks (VANET’s). This paper illustrates the identifying and monitoring the multiple vehicles using deep learning for driving assistance. Kalman filtering is used for monitoring the objects with the pre-trained COCO dataset. Pipeline simplifies the object detection by producing the bounding box around the vehicle. This approach quickly detects the moving vehicle from the running video with the bounding box.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

The  research received no  specific  grant  from  any  funding  agency  in  the  public, commercial, or not-for-profit sectors.

Data Fabrication/Falsification Statement

The author(s) declare that no data have been fabricated, falsified, or manipulated in this study.

Participant Consent

The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained.

Copyright and Licensing

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