A Pedestrian Tracking Algorithm Based on Multi-Granularity Feature

被引：0

作者：

Wang Z. ^{[1
]}

Miao D. ^{[1
,2
]}

Zhao C. ^{[1
,2
]}

Luo S. ^{[1
]}

Wei Z. ^{[1
,2
]}

机构：

[1] Department of Computer Science and Technology, Tongji University, Shanghai

[2] Key Laboratory of Embedded System and Service Computing, Tongji University, Ministry of Education, Shanghai

来源：

Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2020年 / 57卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Deep learning; Multiple-granularity; Object detection; Pedestrian tracking; Robustness;

D O I：

10.7544/issn1000-1239.2020.20190280

中图分类号：

学科分类号：

摘要：

Recently in some popular applications, such as video scene surveillance, long-term effective pedestrian tracking is the basis of these applications. Although the related technology of target detection and target tracking have a long history, how to achieve real-time and accurate pedestrian tracking is still an active research field and needs to be solved. At present, most pedestrian tracking methods only use hand-designed features to track or only use deep learning to extract features, which are not good ways to represent the features of the target because the use of one single feature will restrict the expression of the features. Therefore, multi-granularity hierarchical features are used in this paper to achieve more stable pedestrian tracking. This paper proposes an improved pedestrian tracking algorithm. The algorithm adopts the idea of multi-granularity, combines convolutional feature with bottom color feature, makes decision on the tracking result obtained by GOTURN, a tracking algorithm based on deep learning, and modifies the tracking result with target detection. This paper uses Pascal VOC data set for model training, and uses OTB-100 and VOT 2015 data sets for testing. The experimental results show that the tracking algorithm based on multi-granularity decision can track target pedestrians more accurately than a single tracking algorithm and the tracking accuracy is improved obviously. © 2020, Science Press. All right reserved.

引用

页码：996 / 1002

页数：6

共 19 条

[1]

Danelljan M., Bhat G., Khan F.S., Et al., ECO: Efficient convolution operators for tracking, Proc of the 30th IEEE Conf on Computer Vision and Pattern Recognition, pp. 6638-6646, (2017)

[2]

Zhang H., Hu S., Yang G., Video object tracking based on appearance models learning, Journal of Computer Research and Development, 52, 1, pp. 177-190, (2015)

[3]

Kaaniche M.B., Bremond F., Tracking hog descriptors for gesture recognition, Proc of the 6th IEEE Int Conf on Advanced Video and Signal Based Surveillance, pp. 140-145, (2009)

[4]

LeCun Y., Bengio Y., Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)

[5]

Wang N., Yeung D.Y., Learning a deep compact image representation for visual tracking, Proc of the 26th Advances in Neural Information Processing Systems, pp. 809-817, (2013)

[6]

Wang N., Li S., Gupta A., Et al., Transferring rich feature hierarchies for robust visual tracking, (2015)

[7]

Long J., Shelhamer E., Darrell T., Fully convolutional networks for semantic segmentation, Proc of the 28th IEEE Conf on Computer Vision and Pattern Recognition, pp. 3431-3440, (2015)

[8]

Nam H., Han B., Learning multi-domain convolutional neural networks for visual tracking, Proc of the 29th IEEE Conf on Computer Vision and Pattern Recognition, pp. 4293-4302, (2016)

[9]

Held D., Thrun S., Savarese S., Learning to track at 100 fps with deep regression networks, Proc of the 14th European Conf on Computer Vision, pp. 749-765, (2016)

[10]

Ren S., He K., Girshick R., Et al., Faster R-CNN: Towards real-time object detection with region proposal networks, Proc of the 28th Advances in Neural Information Processing Systems, pp. 91-99, (2015)

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