ORCID Profile
0000-0002-5196-4921
Current Organisation
Jinling Institute of Technology
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Publisher: IEEE
Date: 09-2013
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ACPR.2017.25
Publisher: American Chemical Society (ACS)
Date: 22-06-2023
Publisher: Elsevier BV
Date: 07-2011
Publisher: Hindawi Limited
Date: 20-09-2021
DOI: 10.1155/2021/4427945
Abstract: In this paper, we address the problem of online updating of visual object tracker for car sharing services. The key idea is to adjust the updating rate adaptively according to the tracking performance of the current frame. Instead of setting a fixed weight for all the frames in the updating of the object model, we assign the current frame a larger weight if its corresponding tracking result is relatively accurate and unbroken and a smaller weight on the contrary. To implement it, the current estimated bounding box’s intersection over union (IOU) is calculated by an IOU predictor which is trained offline on a large number of image pairs and used as a guidance to adjust the updating weights online. Finally, we imbed the proposed model update strategy in a lightweight baseline tracker. Experiment results on both traffic and nontraffic datasets verify that though the error of predicted IOU is inevitable, the proposed method can still improve the accuracy of object tracking compared with the baseline object tracker.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 10-2020
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 12-2011
Publisher: World Scientific Pub Co Pte Lt
Date: 21-02-2018
DOI: 10.1142/S0218001418500180
Abstract: In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 08-2018
Publisher: SPIE-Intl Soc Optical Eng
Date: 07-06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
No related grants have been discovered for Haifeng Zhao.