Log In Sign Up

Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks

by   Icaro O. de Oliveira, et al.

This work addresses the problem of vehicle re-identification through a network of non-overlapping cameras. As our main contribution, we propose a novel two-stream convolutional neural network (CNN) that simultaneously uses two of the most distinctive and persistent features available: the vehicle appearance and its license plate. This is an attempt to tackle a major problem, false alarms caused by vehicles with similar design or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for optical character recognition (OCR) to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. As part of this work, we created an important dataset for vehicle re-identification with more than three hours of videos spanning almost 3,000 vehicles. In our experiments, we achieved a precision, recall and F -score values of 99.6 contribution, we discuss and compare three alternative architectures that explore the same features but using additional streams and temporal information. The proposed architectures, trained models, and dataset are publicly available at .


page 1

page 2

page 4

page 6

page 7

page 8

page 9

page 11


A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras

We describe in this paper a novel Two-Stream Siamese Neural Network for ...

Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

In this letter, we propose a pseudo-siamese convolutional neural network...

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

In this paper, we propose a three-stream adaptive fusion network named T...

Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs

Recognizing driver behaviors is becoming vital for in-vehicle systems th...

Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning

In recent years, the development of robust Intelligent transportation sy...

Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture

Buses and heavy vehicles have more blind spots compared to cars and othe...

Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification

Vehicle re-identification (Re-ID) distinguishes between the same vehicle...

Code Repositories


Vehicle-Rear: A New Dataset to Explore Feature Fusion For Vehicle Identification Using Convolutional Neural Networks

view repo


Demo code for journal "Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks".

view repo



view repo