A Comparative Study of Object Tracking using CNN and SDAE

Wei Yang; Wei Wang; Yang Gao; Zhanpeng Jin

doi:10.1109/IJCNN.2018.8489742

A Comparative Study of Object Tracking using CNN and SDAE

Wei Yang
, Wei Wang
, Yang Gao
, Zhanpeng Jin

State University of New York System

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.

Original language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489742
State	Published - 10 Oct 2018
Externally published	Yes
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Keywords

convolutional neural network
object tracking
particle filter
stacked denoising autoencoder

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10.1109/IJCNN.2018.8489742

Cite this

Yang, W., Wang, W., Gao, Y., & Jin, Z. (2018). A Comparative Study of Object Tracking using CNN and SDAE. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489742 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489742

@inproceedings{dafc33f277794971ac7c09e1095945c1,

title = "A Comparative Study of Object Tracking using CNN and SDAE",

abstract = "Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.",

keywords = "convolutional neural network, object tracking, particle filter, stacked denoising autoencoder",

author = "Wei Yang and Wei Wang and Yang Gao and Zhanpeng Jin",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8489742",

language = "英语",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",

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}

Yang, W, Wang, W, Gao, Y & Jin, Z 2018, A Comparative Study of Object Tracking using CNN and SDAE. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489742, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489742

A Comparative Study of Object Tracking using CNN and SDAE. / Yang, Wei; Wang, Wei; Gao, Yang et al.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489742 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Comparative Study of Object Tracking using CNN and SDAE

AU - Yang, Wei

AU - Wang, Wei

AU - Gao, Yang

AU - Jin, Zhanpeng

PY - 2018/10/10

Y1 - 2018/10/10

N2 - Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.

AB - Object tracking which refers to automatic estimation of the trajectory is a challenging problem. To track the object robustly and efficiently, we explored an autonomous object tracking methodological framework that adopts the deep learning architectures, specifically the convolutional neural network (CNN) and the stacked denoising autoencoder (SDAE), as opposed to the most frequently used tracking algorithms that only learn the appearance of the tracked object. Moreover, we conduct a comparative study of both approaches in terms of tracking accuracy and efficiency. The results show that the features learned by both CNN and SDAE are very supportive in object tracking problem and the detailed comparisons are demonstrated in this work.

KW - convolutional neural network

KW - object tracking

KW - particle filter

KW - stacked denoising autoencoder

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U2 - 10.1109/IJCNN.2018.8489742

DO - 10.1109/IJCNN.2018.8489742

M3 - 会议稿件

AN - SCOPUS:85056532989

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

A Comparative Study of Object Tracking using CNN and SDAE

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