Efficient and Deep Vehicle Re-Identification Using Multi-Level Feature Extraction

Muhammad Khokhar,Rajesh Kumar,Zakria Zakria,Jingye Cai,Jianhua Deng,Muhammad Aftab

doi:10.3390/app9071291

Abstract

The intelligent transportation system is currently an active research area, and vehicle re-identification (Re-Id) is a fundamental task to implement it. It determines whether the given vehicle image obtained from one camera has already appeared over a camera network or not. There are many possible practical applications where the vehicle Re-Id system can be employed, such as intelligent vehicle parking, suspicious vehicle tracking, vehicle incident detection, vehicle counting, and automatic toll collection. This task becomes more challenging because of intra-class similarity, viewpoint changes, and inconsistent environmental conditions. In this paper, we propose a novel approach that re-identifies a vehicle in two steps: first we shortlist the vehicle from a gallery set on the basis of appearance, and then in the second step we verify the shortlisted vehicle’s license plates with a query image to identify the targeted vehicle. In our model, the global channel extracts the feature vector from the whole vehicle image, and the local region channel extracts more discriminative and salient features from different regions. In addition to this, we jointly incorporate attributes like model, type, and color, etc. Lastly, we use a siamese neural network to verify license plates to reach the exact vehicle. Extensive experimental results on the benchmark dataset VeRi-776 demonstrate the effectiveness of the proposed model as compared to various state-of-the-art methods.

Highlights

Nowadays, the security and safety of objects and public place is an important issue in society.Government and private sector organizations are trying to overcome this issue on a priority basis
We developed the whole framework in Python and most of the time the libraries used were NumPy, matpotlib, scikit-learn, and SciPy
We have used mean average precision (mAP) and cumulative matching characteristics (CMC) for evaluation of the technique; the mAP metric evaluates the overall performance for Re-Id. mAP was calculated for each probe image as follows: n

Summary

Introduction

The security and safety of objects and public place is an important issue in society.Government and private sector organizations are trying to overcome this issue on a priority basis. Suppose we want to locate or to track a vehicle in a metropolitan city In this situation, surveillance cameras play an important role, but continuous recording videos of surveillance cameras generate a large amount of data and it is hard for an operator to analyze the data whenever any specific incident happens. An intelligent video surveillance system’s goal is to automatically monitor and analyze the data to help an operator in understanding the acquired video of a surveillance camera [1,2] Those cameras observe changes in pixels due to the object crossing from the field of view of a camera. According to this scenario, it is only implemented in those areas where there is a rare movement of vehicles or in a parking area [1,3]. Changes in color response: Color is one of the important parameters in vehicle Re-Id but a camera to camera color response varies due to camera features and settings [16] and this may affect vehicle appearance.

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Conclusion