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Vehicle re-identification for a parking lot toll system using convolutional neural networks
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Paper Abstract

We present a vehicle re-identification method for a parking lot toll system. Given a probe image captured from one camera installed in the entrance of a parking lot, re-identification is the method of identifying a matching image from a gallery set constructed from different cameras in the exit region. This method is especially useful when the license plate recognition fails. Our method is based on a convolutional neural network (CNN) which is a variant of multilayer perceptron (MLP). An input image of the CNN model is cropped by the license plate detection (LPD) algorithm to eliminate the background of an original image. To train a vehicle re-identification model, we adopt the pre-trained models which showed the outstanding results in the ImageNet [1] challenge from 2014 to 2015. Then, we fine-tune one of the models (GoogLeNet [2]) for a car’s model recognition task using a large-scale car dataset [3]. This fine-tuned model is utilized as a feature extractor. Cosine function is used to measure the similarity between a probe and a gallery. To evaluate the performance of our method, we create two datasets: ETRI-VEHICLE-2016-1 and ETRI-VEHICLE2016-2. The experimental result reveals that the proposed technique can achieve promising results.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412K (15 March 2019); doi: 10.1117/12.2522748
Show Author Affiliations
Dongjin Lee, Electronics and Telecommunications Research Institute (Korea, Republic of)
Chungnam National Univ. (Korea, Republic of)
Yongwoo Jo, Electronics and Telecommunications Research Institute (Korea, Republic of)
Seung-Jun Han, Electronics and Telecommunications Research Institute (Korea, Republic of)
Jungyu Kang, Electronics and Telecommunications Research Institute (Korea, Republic of)
Kyoungwook Min, Electronics and Telecommunications Research Institute (Korea, Republic of)
Jeongdan Choi, Electronics and Telecommunications Research Institute (Korea, Republic of)
Cheong Hee Park, Chunghan National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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