自适应权值卷积特征的鲁棒目标跟踪算法

doi:10.19665/j.issn1001-2400.2019.01.019

Abstract

Abstract:

To solve the tracking failure problem in some videos caused by traditional deep learning tracking algorithms with fixed weight convolutional features, this paper proposes a novel tracking method combing the response map and the entropy function which considers the performance of each layer of convolutional neural networks and automatically adjusts the weight parameters. At the same time, an EdgeBoxes detection scheme is introduced when the maximum value of tracking response is less than a given threshold. A great number of bounding boxes are extracted by a sliding window and are evaluated by the EdgeBoxes detection scheme which generates the original proposal bounding boxes. Finally, the tracking method based on the correlation filter are conducted on the original proposal bounding boxes with the update scheme given. We have tested the proposed algorithm and nine state-of-the-art approaches on OTB-2013 video databases. Experimental results demonstrate that the proposed method has a higher precision and overlap rate.

Key words: object tracking, adaptive weight, correlation filters, object detection

CLC Number:

TP391

WANG Haijun,ZHANG Shengyan. Robust object tracking via adaptive weight convolutional features[J].Journal of Xidian University, 2019, 46(1): 117-123.

Figures/Tables 4

References 17

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算法	CT	CSK	KCF	DCF_CA	DLSSVM	MEEM	HDT	ASLA	HCFT	Ours
IV	0.295	0.369	0.493	0.530	0.540	0.533	0.557³	0.429	0.560²	0.606¹
OPR	0.297	0.386	0.495	0.536	0.582	0.558	0.584³	0.422	0.587²	0.612¹
SV	0.302	0.350	0.427	0.466	0.494	0.498	0.523³	0.452	0.531²	0.557¹
OC	0.321	0.365	0.514	0.537	0.589	0.552	0.603³	0.376	0.606²	0.609¹
DE	0.345	0.343	0.534	0.576	0.632	0.560	0.627²	0.372	0.626³	0.670¹
MB	0.269	0.305	0.497	0.532	0.578	0.541	0.614²	0.258	0.616¹	0.581³
FM	0.298	0.316	0.459	0.497	0.553	0.553	0.574²	0.247	0.578¹	0.565³
IPR	0.282	0.399	0.497	0.547	0.556	0.535	0.580³	0.425	0.582²	0.605¹
OV	0.359	0.349	0.550	0.543	0.581²	0.606¹	0.569	0.312	0.575³	0.539
BC	0.273	0.421	0.535	0.582	0.592	0.569	0.610³	0.408	0.623²	0.650¹
LR	0.149	0.256	0.272	0.345	0.380	0.401	0.426	0.427³	0.442²	0.496¹

Robust object tracking via adaptive weight convolutional features

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