Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing

LI Yaqian; JIA Lu; LI Haibin; ZHANG Wenming; ZHANG Yansong

doi:10.11999/JEIT170745

Volume 40 Issue 5

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1242-1249

LI Yaqian, JIA Lu, LI Haibin, ZHANG Wenming, ZHANG Yansong. Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1242-1249. doi: 10.11999/JEIT170745

Citation:

LI Yaqian, JIA Lu, LI Haibin, ZHANG Wenming, ZHANG Yansong. Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1242-1249. doi: 10.11999/JEIT170745

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Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing

doi: 10.11999/JEIT170745

Funds:

The Natural Science Foundation of Hebei Province (F2015203212)

Received Date: 2017-07-21
Rev Recd Date: 2018-01-24
Publish Date: 2018-05-19

Abstract

Abstract

For object detection in fisheye images which present serious distortion, an object tracking method is proposed to deal with scale variance, pose change and distortion. Firstly, gray feature and gradient feature are combined to obtain a high dimensional feature of the target, then reduce its dimensionality by averaging to obtain targets compressive feature. According to fisheye imaging model, motion of object point is modeled, and range of motion of target is predicted. In order to adjust to scale variance, corner points are positioned respectively in a coarse to fine manner based on the block matching motion estimation, and the scale of compressed feature is changed along with scale change of object box. Experimental results show that the proposed algorithm is superior to other algorithms in the case of distortion, scale change, pose change and part occlusion.
- Target tracking,
- Fisheye imaging model,
- Gradient feature,
- Compression feature,
- Block matching motion estimation

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References(22)

References

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