A study of CNN models for re-identification of vehicles
- Title
- A study of CNN models for re-identification of vehicles
- Creator
- Mathews M.; Sethilnathan T.
- Description
- Vehicle Re-identification has evolved in recent times. Initially, clicking a single picture of a vehicle or a car was done manually, inviting the workforce to complete a specified task. With the growth in technology, the method and techniques in Vehicle Re-Id also have advanced, transforming from manual to automation. Surveillance cameras were used to capture vehicle images and retrieve information about a specific vehicle. Re-trieving and identifying the images of the vehicle is done using computer vision, the most important branch of computer science and artificial intelligence. Earlier, Vehicle Re-Id implemented a single algorithm on a dataset, making the corresponding result insufficient to determine its effects. This paper proposes a brief survey of multi-modal techniques and methods for vehicle re-identification and fingerprinting. The different attributes of the vehicle are considered for ANPR (Automatic number plate recognition) for identifying the number plate, focusing on the vehicle's details or features as the initial phase of identification, and then the vehicle number plate. 2023 IEEE.
- Source
- 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Networks; Datasets; Deep Learning; Object Character Recognition; Vehicle Re-identification
- Coverage
- Mathews M., CHRIST (Deemed to Be University), Bangalore, India; Sethilnathan T., CHRIST (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039763-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mathews M.; Sethilnathan T., “A study of CNN models for re-identification of vehicles,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 1, 2025, https://archives.christuniversity.in/items/show/19915.