A real-time unmanned aerial vehicle (UAV) object tracking algorithm based on the response and filter deviation-aware regularization was proposed, aiming at the problem that targets were easily subject to the huge variation of appearance and various change of viewpoint interference in the UAV sequences. According to the consistency of response and correlation filter difference between video frames, the variation of correlation response and filter difference were modeled. Furthermore, an objective function with constraint scheme was constructed, which can learn variation of object appearance and filter. Meanwhile, an auxiliary variable based on the response and filter deviation-aware regularization was introduced to build an optimization function and alternating direction method of multipliers (ADMM) was used to optimize the solution of the correlation filter and auxiliary variable. To validate the effectiveness of the proposed algorithm, comparison experiments with other 9 algorithms were performed on three UAV tracking benchmarks, including DTB70、UAV123@10 fps and UAVDT, in terms of precision and success rate. Experimental results show that the proposed algorithm has good robustness for occlusion, deformation and view variation and can effectively track the target with an average speed of 39.0 frames of second.
Hai-jun WANG,Sheng-yan ZHANG,Yu-jie DU. UAV object tracking algorithm based on response and filter deviation-aware regularization. Journal of ZheJiang University (Engineering Science), 2022, 56(9): 1824-1832.
Tab.1Comparison in terms of precision and success rates for ten tracking algorithms on three UAV datasets
算法
P
ARV
BC
DEF
FCM
IPR
MB
OCC
OPR
OV
SV
SOA
RDAR
0.600
0.589
0.656
0.705
0.613
0.675
0.582
0.434
0.628
0.661
0.700
BACF
0.392
0.545
0.448
0.636
0.547
0.639
0.515
0.266
0.650
0.533
0.624
MCCT_H
0.495
0.484
0.550
0.621
0.552
0.502
0.571
0.383
0.573
0.643
0.606
ECO-HC
0.506
0.567
0.584
0.680
0.568
0.640
0.641
0.430
0.557
0.530
0.667
SRDCFdecon
0.343
0.449
0.283
0.574
0.430
0.500
0.456
0.193
0.570
0.473
0.564
Tab.2Comparison in terms of precision of five tracking algorithms for different attributes on DTB70 dataset
算法
P
ARC
BC
CM
FM
FOC
IV
LR
OV
POC
SV
SOB
VC
RDAR
0.587
0.469
0.635
0.531
0.464
0.568
0.568
0.547
0.613
0.630
0.678
0.591
BACF
0.478
0.425
0.532
0.407
0.336
0.430
0.431
0.421
0.467
0.525
0.605
0.491
MCCT_H
0.493
0.469
0.544
0.361
0.421
0.477
0.455
0.493
0.542
0.547
0.627
0.484
ECO-HC
0.558
0.511
0.609
0.487
0.454
0.507
0.527
0.522
0.556
0.587
0.637
0.548
SRDCFdecon
0.476
0.427
0.536
0.403
0.427
0.423
0.436
0.483
0.514
0.535
0.621
0.478
Tab.3Comparison in terms of precision of five tracking algorithms for different attributes on UAV123@10 pfs dataset
算法
P
BC
CM
IV
LO
LTT
OB
OM
SV
SO
RDAR
0.632
0.687
0.761
0.549
0.877
0.720
0.641
0.647
0.808
BACF
0.599
0.614
0.739
0.488
0.886
0.699
0.604
0.604
0.770
MCCT_H
0.571
0.622
0.703
0.482
0.925
0.667
0.561
0.594
0.796
ECO-HC
0.607
0.647
0.723
0.504
0.924
0.669
0.596
0.607
0.767
SRDCFdecon
0.533
0.588
0.690
0.433
0.812
0.650
0.560
0.565
0.716
Tab.4Comparison in terms of precision of five tracking algorithms for different attributes on UAVDT dataset
算法
Rs
ARV
BC
DEF
FCM
IPR
MB
OCC
OPR
OV
SV
SOA
RDAR
0.396
0.379
0.434
0.483
0.420
0.458
0.413
0.318
0.407
0.465
0.467
BACF
0.273
0.337
0.302
0.436
0.371
0.412
0.348
0.203
0.419
0.392
0.411
MCCT_H
0.334
0.296
0.354
0.410
0.376
0.334
0.377
0.243
0.349
0.439
0.399
ECO-HC
0.376
0.349
0.404
0.469
0.410
0.434
0.431
0.319
0.416
0.430
0.446
SRDCFdecon
0.250
0.285
0.196
0.398
0.311
0.331
0.308
0.150
0.384
0.351
0.369
Tab.5Comparison in terms of success rate of five tracking algorithms for different attributes on DTB70 dataset
算法
Rs
ARC
BC
CM
FM
FOC
IV
LR
OV
POC
SV
SOB
VC
RDAR
0.412
0.302
0.464
0.355
0.257
0.391
0.342
0.404
0.427
0.453
0.478
0.422
BACF
0.334
0.275
0.397
0.275
0.173
0.310
0.248
0.321
0.327
0.374
0.424
0.353
MCCT_H
0.357
0.305
0.407
0.260
0.236
0.342
0.257
0.365
0.376
0.396
0.451
0.361
ECO-HC
0.392
0.339
0.449
0.332
0.247
0.362
0.299
0.387
0.391
0.424
0.464
0.400
SRDCFdecon
0.344
0.293
0.399
0.276
0.231
0.314
0.247
0.356
0.361
0.390
0.440
0.360
Tab.6Comparison in terms of success rate of five tracking algorithms for different attributes on UAV123@10 pfs dataset
Fig.1Comparison of representative tracking results by different algorithms on DTB70、UAV123@10 fps、UAVDT
算法
Rs
BC
CM
IV
LO
LTT
OB
OM
SV
SO
RDAR
0.399
0.435
0.465
0.386
0.577
0.442
0.397
0.435
0.464
BACF
0.367
0.387
0.460
0.340
0.582
0.443
0.371
0.408
0.428
MCCT_H
0.343
0.367
0.415
0.348
0.565
0.390
0.343
0.384
0.389
ECO-HC
0.364
0.379
0.434
0.348
0.573
0.391
0.358
0.390
0.375
SRDCFdecon
0.339
0.374
0.430
0.322
0.515
0.395
0.351
0.389
0.410
Tab.7Comparison in terms of success rate of five tracking algorithms for different attributes on UAVDT dataset
算法
v/(帧·s?1)
算法
v/(帧·s?1)
RDAR
39.0
SAMF_CA
9.1
ECO-HC
62.2
SAMF
10.0
MCCT_H
59.0
SRDCF
10.7
BACF
46.5
SRDCFdecon
6.0
fDSST
132.0
Staple
62.5
Tab.8Comparison of tracking speed for different tracking algorithms on DTB70 dataset
Fig.2Comparison of tracking performance on UAVDT with different parameters
模块
P
Rs
DTB70
UAV123@10 fps
UAVDT
DTB70
UAV123@10 fps
UAVDT
BACF
0.590
0.572
0.686
0.402
0.413
0.433
BACF+RD
0.648
0.663
0.713
0.451
0.484
0.457
BACF+FD
0.667
0.649
0.671
0.458
0.470
0.453
RDAR(BACF+RD+CF)
0.671
0.672
0.724
0.462
0.488
0.458
Tab.9Comparison of tracking performance with different modules on three UAV datasets
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