Siamese Object Tracking Algorithm Combined with the Intersection over Union Loss

doi:10.16182/j.issn1004731x.joss.21-0377

Abstract

Abstract:

To improve the accuracy of the object bounding box regression prediction of the SiamRPN,solve the problem of low discrimination of positive samples in classification prediction and the lack of correlation between regression prediction and classification prediction, an improved object tracking algorithm of SiamRPN which combined with IoU(intersection over union) loss is proposed. A joint optimization module of IoU-smooth L1 is designed to optimize the IoU loss of the best positive sample and the smooth L1 loss of other positive samples jointly. According to the regression prediction results, the weighted classification prediction is performed on the positive samples with the weight calculated by the IoU of the prediction box and the truth box, so as to increase the discrimination between the positive samples, while ensuring the correlation between the classification prediction and the regression prediction.The results show that the proposed algorithm can effectively improve the tracking performance.

Key words: machine vision, object tracking, siamese network, anchor boxes, inersection over union loss

CLC Number:

TP391.4

Wei Zhou, Yuxiang Liu, Guangping Liao, Xin Ma. Siamese Object Tracking Algorithm Combined with the Intersection over Union Loss[J]. Journal of System Simulation, 2022, 34(09): 1956-1967.

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网络层类型	尺寸/步长	卷积核数量
Conv1	11×11/2	96
Pool1	3×3/2
Conv2	5×5/1	256
Pool2	3×3/2
Conv3	3×3/1	384
Conv4	3×3/1	384
Conv5	3×3/1	256

跟踪算法	准确率	鲁棒性	EAO	FPS
Staple	0.531	0.632	0.186	77
KCF	0.467	1.302	0.097	172
CSRDCF	0.505	0.487	0.211	13
STRCF	0.480	0.613	0.162	32
SiamRPN	0.532	0.459	0.247	160
SiamFC	0.492	0.609	0.199	101
SiamIoU	0.521	0.379	0.276	153