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

Applied Sciences

Muhammad Saddam Khokhar¹  Rajesh Kumar²  Jingye Cai³  Zakria³  Jianhua Deng³  Muhammad Umar Aftab³ 

[1] School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;

关键词: vehicle re-identification;    overlapping and non-overlapping cameras;    global and local features;    deep learning;    license plate;   

DOI  :  10.3390/app9071291

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown