Abstract:Most of the tracking-by-detection algorithms treat the tracking task as a category classification task, when the target experience deformation or encounter similar objects interference, the model drift is prone to occur. In this paper, a multi-exemplar regression tracking algorithm is proposed. In this algorithm, the exemplar model is considered to be more appropriate for tracking task, the exemplar model is set up by a frame image information, and the multi-exemplar model established in the time series can represent the target current state; in order to make the tracking algorithm adapt to the target deformation, the exemplar model is considered as the hidden variable by logistic regression model, together with the training sets from several recent frames sampling, can jointly build multi-exemplar regression tracking model. As the tracker builds multi-exemplar model on the whole, linking them together closely, it can effectively deal with the target deformation. Since the model drift only affects the exemplar model at current frame, each exemplar model is independent of each other, so the tracking algorithm can effectively reduce the influence of model drift on robust tracking. In the experiment, OTB 2013 benchmark and UAV 123 benchmark are used to verify the algorithm, DeepSRDCF, Siamese-fc and other algorithms act as the contrast algorithms, the experimental results show that the proposed tracker not only gives full play to the advantages of tracking based on multi-exemplar regression model, but also has good performance in deformation and background blur scene, and achieves three to five percent more than other advanced algorithms in the metrics of success rate and precision.
BI Duyan, KU Tao, ZHA Yufei, et al. Scale-adaptive objecttracking based on color names histogram[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1099-1106. doi: 10.11999/JEIT150921
[2]
HARE S, SAFFARI A, TORR P H S, et al. Struck: Structured output tracking with kernels[C]. IEEE International Conference on Computer Vision, Barcelona, Spain, 2011: 263-270.
[3]
ZHU G, PORIKLI F, and LI H. Beyond local search: Tracking objects everywhere with instance-specific proposals [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA, 2016: 943-951.
[4]
DANELLJAN M, HAGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]. International Conference on Computer Vision, Santiago, Chile, 2015: 4310-4318.
[5]
BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: Complementary learners for real-time tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 1401-1409.
[6]
GAO C, CHEN F, YU J G, et al. Robust visual tracking using exemplar-based detectors[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2017, 27(2): 300-312. doi: 10.1109/TCSVT.2015.2513700
[7]
MALISIEWICZ T, GUPTA A, and ROS A A. Ensemble of exemplar-SVMs for object detection and beyond[C]. International Conference on Computer Vision, Barcelona, Spain, 2011: 89-96.
[8]
TAO R, GAVVES E, and SMEULDER A W M. Siamese instance search for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 1420-1429.
[9]
BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 850-865.
[10]
CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details: Delving deep into convolutional nets[OL]. https://arxiv.org/abs/1405.3531, 2014.
[11]
DENG J, DONG W, SOCHER R, et al. Imagenet: A large- scale hierarchical image database[C]. Computer Vision and Pattern Recognition, Miami, FL, USA, 2009: 248-255.
[12]
PLATT J. Sequential minimal optimization: A fast algorithm for training support vector machines[J]. International Journal of Advanced Computer Science and Applications, 1998, 208(14): 98-112.
[13]
NAM H, BAEK M, and HAN B. Modeling and propagating cnns in a tree structure for visual tracking[OL]. https://arxiv. org/abs/1608.07242.
[14]
NAM H and HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 4293-4302.
[15]
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 580-587.
[16]
FAN R E, CHANG K W, HSIEH C J, et al. LIBLINEAR: A library for large linear classification[J]. Journal of Machine Learning Research, 2008, 9(8): 1871-1874.
[17]
WU Y, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Oregon, USA, 2013: 2411-2418.
[18]
MUELLER M, SMITH N, and GHANEM B. A benchmark and simulator for uav tracking[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 445-461.
[19]
DANELLJAN M, HAGER G, KHAN F S, et al. Convolutional features for correlation filter based visual tracking[C]. International Conference on Computer Vision, Santiago, Chile, 2015: 621-629.
[20]
ZHANG J, MA S, and SCLAROFF S. MEEM: Robust tracking via multiple experts using entropy minimization[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 188-203.
[21]
HONG Z, CHEN Z, WANG C, et al. Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 749-758.
[22]
DALAL N and TRIGGS B. Histograms of oriented gradients for human detection[C]. Computer Vision and Pattern Recognition, San Diego, CA, USA, 2005: 886-893.