FDnCNN-based image denoising for multi-labfel localization measurement

Book sorting system is one of specific application in smart library scenarios, and it now has been widely used in most libraries based on RFID (radio-frequency identification devices) technology. Book identification processing is one of the core parts of a book sorting system, and the efficiency and accuracy of book identification are extremely critical to all libraries. In this paper, the authors propose a new image recognition method to identify books in libraries based on barcode decoding together with deep learning optical character recognition (OCR) and describe its application in library book identification processing.,The identification process relies on recognition of the images or videos of the book cover moving on a conveyor belt. Barcode is printed on or attached to the surface of each book. Deep learning OCR program is applied to improve the accuracy of recognition, especially when the barcode is blurred or faded. The approach the authors proposed is robust with high accuracy and good performance, even though input pictures are not in high resolution and the book covers are not always vertical.,The proposed method with deep learning OCR achieves best accuracy in different vertical, skewed and blurred image conditions.,Methods that the authors proposed need to cooperate and practice in different book sorting machine.,The authors collected more than 500 books from a library. These photos display the cover of more than 100 randomly picked books with backgrounds in different colors, each of which has about five different pictures captured from variety angles. The proposed method combines traditional barcode identification algorithm with the authors’ modification to locate and deskew the image. And deep learning OCR is involved to enhance the accuracy when the barcode is blurred or partly faded. Book sorting system design based on this method will also be introduced.,Experiment demonstrates that the accuracy of the proposed method is high in real-time test and achieves good accuracy even when the barcode is blurred. Deep learning is very effective in analyzing image content, and a corresponding series of methods have been formed in video content understanding, which can be a greater advantage and play a role in the application scene of intelligent library.

Smart library book sorting application with intelligence computer vision technology

The quality of multi-tag imaging greatly affects the effective detection of multi-tag. When multi-tag moves rapidly, the image may have serious dynamic blur, and tags can not be detected efficientl...

RFID multi-tag dynamic detection measurement based on conditional generative adversarial nets and YOLOv3:

RFID tag group recognition based on motion blur estimation and YOLOv2 improved by Gaussian

The dynamic non-uniform blur caused by Radio Frequency Identification (RFID) multi-label motion seriously affects the identification and location of labels. It is an ill-posed inverse problem for that the blur kernel and sharp image are unknown. The traditional method of removing the blur is very time-consuming. In this work, we propose Multi-scale Recursive Codec Network based on the Authority Parameter (MRCN-AP) to deblur RFID multi-label images in a vision-based RFID multi-label 3D measurement system. This network is composed of a stack of three encoder-decoder subnets of different scales, which restores the blurry image in an end-to-end manner, and extracts the detail edge on each scale effectively from coarse to fine. The proposed authority parameters reduce the parameters memory of redundant networks and improve the speed of the deblurring network. Also, we propose new large-scale RFID multi-label blur-sharp image pairs captured by the dual CCD camera. The proposed model is implemented on an extended dataset. We prove that our method improves the speed by at least 0.55 s, and also increases Peak Signal to Noise Ratio (PSNR) by 2.43dB. Besides, better visual effects are obtained by MRCN-AP deblurring network for RFID multi-label image, which is more conducive to subsequent positioning and optimization.

Multi-scale recursive codec network with authority parameters (MRCN-AP) for RFID multi-label deblurring

Real-time identifying and tracking monitored objects is an important application in a public safety scenario. Both Radio Frequency Identification (RFID) and computer vision are potential solutions to monitor objects while faced with respective limitations. In this paper, we combine RFID and computer vision to propose a hybrid indoor tracking system, which can efficiently identify and track the monitored object in the scene with people gathering and occlusion. In order to get a high precision and robustness trajectory, we leverage Dempster-Shafer (DS) evidence theory to effectively fuse RFID and computer vision based on the prior probability error distribution. Furthermore, to overcome the drift problem under long-occlusion, we exploit the feedback from the high-confidence tracking results and the RFID signals to correct the false visual tracking. We implement a real-setting tracking prototype system to testify the performance of our proposed scheme with the off-the-shelf IP network camera, as well as the RFID devices. Experimental results show that our solution can achieve 98% identification accuracy and centimeter-level tracking precision, even in long-term occlusion scenarios, which can manipulate various practical object-monitoring scenarios in the public security applications.

Fusing RFID and Computer Vision for Occlusion-Aware Object Identifying and Tracking

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Fusing RFID and Computer Vision for Occlusion-Aware Object Identifying and Tracking