Accurate object tracking algorithm based on distance weighting overlap prediction and ellipse fitting optimization

doi:10.11772/j.issn.1001-9081.2020060869

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1100-1105.DOI: 10.11772/j.issn.1001-9081.2020060869

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Accurate object tracking algorithm based on distance weighting overlap prediction and ellipse fitting optimization

WANG Ning¹, SONG Huihui², ZHANG Kaihua¹

1. Jiangsu Key Laboratory of Big Data Analysis Technology;(Nanjing University of Information Science and Technology), Nanjing Jiangsu 210044, China;
2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology;(Nanjing University of Information Science and Technology), Nanjing Jiangsu 210044, China

Received:2020-06-23 Revised:2020-11-27 Online:2021-04-10 Published:2020-12-30
Supported by:
This work is partially supported by the National New Generation Artificial Intelligence Major Project of China (2018AAA0100400), the National Natural Science Foundation of China (61872189, 61876088), the Natural Science Foundation of Jiangsu Province (BK20191397).

基于距离加权重叠度估计与椭圆拟合优化的精确目标跟踪算法

王宁¹, 宋慧慧², 张开华¹

1. 江苏省大数据分析技术重点实验室(南京信息工程大学), 南京 210044;
2. 江苏省大气环境与装备技术协同创新中心(南京信息工程大学), 南京 210044

通讯作者: 宋慧慧
作者简介:王宁（1995—），男，河北邢台人，硕士研究生，主要研究方向：视觉目标跟踪；宋慧慧（1986—），女，山东聊城人，教授，博士，主要研究方向：遥感图像处理；张开华（1983—），男，山东日照人，教授，博士，CCF会员，主要研究方向：图像分割、目标跟踪。
基金资助:
国家新一代人工智能重大项目（2018AAA0100400）；国家自然科学基金资助项目（61872189，61876088）；江苏省自然科学基金资助项目（BK20191397）。

Abstract

Abstract: In order to solve the problems of Discriminative Correlation Filter(DCF) tracking algorithm such as model drift, rough scale and tracking failure when the tracking object suffers from rotation or non-rigid deformation, an accurate object tracking algorithm based on Distance Weighting Overlap Prediction and Ellipse Fitting Optimization(DWOP-EFO) was proposed. Firstly, the overlap and center-distance between bounding-boxes were both used as the basis for the evaluation of dynamic anchor boxes, which can narrow the spatial distance between the prediction result and the object region,easing the model drift problem. Secondly,in order to further improve the tracking accuracy,a lightweight object segmentation network was applied to segment the object from background, and the ellipse fitting algorithm was applied to optimize the segmentation contour result and output stable rotated bounding box, achieving accurate estimation of the object scale. Finally, a scale-confidence optimization strategy was used to realize gating output of the scale result with high confidence. The proposed algorithm can alleviate the problem of model drift, enhance the robustness of the tracker, and improve the accuracy of the tracker. Experiments were conducted on two widely used evaluation datasets Visual Object Tracking challenge(VOT2018) and Object Tracking Benchmark(OTB100). Experimental results demonstrate that the proposed algorithm improves Expected-Average-Overlap(EAO) index by 2.2 percentage points compared with Accurate Tracking by Overlap Maximization(ATOM) and by 1.9 percentage points compared with Learning Discriminative Model Prediction for tracking(DiMP). Meanwhile, on evaluation dataset OTB100, the proposed algorithm outperforms ATOM by 1.3 percentage on success rate index and shows significant performance especially on attribute of non-rigid deformation. the proposed algorithm runs over 25 frame/s averagely on evaluation datasets which realizes real-time tracking.

Key words: discriminative correlation filtering, visual tracking, object segmentation, distance weighting, ellipse fitting

摘要： 为解决判别式相关滤波（DCF）跟踪算法在跟踪目标旋转或非刚性形变时的模型漂移、尺度粗糙、跟踪失败问题，提出一种基于距离加权重叠度估计与椭圆拟合优化的精确目标跟踪算法（DWOP-EFO）。首先，同时采用矩形框之间的重叠度和中心距离作为动态锚框质量评价的依据，能够缩小预测结果与目标区域之间的空间距离，缓解模型漂移问题；其次，为了进一步提高跟踪精度，采用轻量化的目标分割网络将目标从背景中分割出来，再利用椭圆拟合算法对分割轮廓进行优化并输出稳定的旋转矩形框，实现对目标尺度的精确估计；最后，通过尺度置信度优化策略对置信度高的尺度结果实现门控输出。所提算法能缓解模型漂移问题，同时有利于增强跟踪器的鲁棒性和提升跟踪精度。在两个最为流行的评测数据集VOT2018和OTB100上进行了实验，结果表明：在VOT2018数据集上，所提算法的期望平均重叠率（EAO）指标比基于重叠度最大化准确跟踪算法（ATOM）提高2.2个百分点，相较于基于可学习的判别模型跟踪器（DiMP）提高1.9个百分点；同时，所提算法在OTB100评测数据集上的成功率指标比ATOM高出1.3个百分点，特别是在非刚性形变属性上效果显著。所提算法在评测数据集上的平均运行速率均超过25 frame/s实现了实时跟踪。

CLC Number:

TP391.4

WANG Ning, SONG Huihui, ZHANG Kaihua. Accurate object tracking algorithm based on distance weighting overlap prediction and ellipse fitting optimization[J]. Journal of Computer Applications, 2021, 41(4): 1100-1105.

王宁, 宋慧慧, 张开华. 基于距离加权重叠度估计与椭圆拟合优化的精确目标跟踪算法[J]. 计算机应用, 2021, 41(4): 1100-1105.

References

[1] 李玺. 查宇飞. 张天柱, 等. 深度学习的目标跟踪算法综述[J]. 中国图象图形学报,2019,24(12):2057-2080.(LI X,ZHA Y F,ZHANG T Z,et al. Survey of visual object tracking algorithms based on deep learning[J]. Journal of Image and Graphics,2019, 24(12):2057-2080.)
[2] KALAL Z,MIKOLAJCZYK K,MATAS J,et al. Tracking-learningdetection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[3] VOIGTLAENDER P,LUITEN J,TORR P H S,et al. Siam RCNN:visual tracking by re-detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2020:6578-6588.
[4] BERTINETTO L,VALMADRE J,HENRIQUES J F,et al. Fullyconvolutional Siamese networks for object tracking[C]//Proceedings of the 2016 European Conference on Computer Vision, LNCS 9914. Cham:Springer,2016:850-865.
[5] 樊佳庆, 宋慧慧, 张开华. 通道稳定性加权补充学习的实时视觉跟踪算法[J]. 计算机应用,2018,38(6):1751-1754.(FAN J Q, SONG H H,ZHANG K H. Real-time visual tracking via channel stability weighted complementary learning[J]. Journal of Computer Applications,2018,38(6):1751-1754.)
[6] LI B,YAN J,WU W,et al. High performance visual tracking with Siamese region proposal network[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2018:8971-8980.
[7] DANELLJAN M,BHAT G,KHAN F S,et al. ECO:efficient convolution operators for tracking[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2017:6931-6939.
[8] BHAT G,JOHNANJER J,DANELLJAN M,et al. Unveiling the power of deep tracking[C]//Proceedings of the 2018 European Conference on Computer Vision,LNCS 11206. Cham:Springer, 2018:483-498.
[9] DANELLJAN M,BHAT G,KHAN F S,et al. ATOM:accurate tracking by overlap maximization[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:4660-4669.
[10] 熊昌镇, 车满强, 王润玲. 基于稀疏卷积特征和相关滤波的实时视觉跟踪算法[J]. 计算机应用,2018,38(8):2175-2179, 2223.(XIONG C Z,CHE M Q,WANG R L. Real-time visual tracking algorithm based on correlation filters and sparse convolutional features[J]. Journal of Computer Applications, 2018,38(8):2175-2179,2223.)
[11] BHAT G, DANELLJAN M, VAN GOOL L, et al. Learning discriminative model prediction for tracking[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE,2019:6181-6190.
[12] YOO D,PARK S,LEE J Y,et al. Multi-scale pyramid pooling for deep convolutional representation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2015:71-80.
[13] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE, 2015:1440-1448.
[14] JIANG B,LUO R,MAO J,et al. Acquisition of localization confidence for accurate object detection[C]//Proceedings of the 2018 European Conference on Computer Vision,LNCS 11218. Cham:Springer,2018:816-832.
[15] ZHENG Z,WANG P,LIU W,et al. Distance-IoU loss:faster and better learning for bounding box regression[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA:AAAI Press,2020:12993-13000.
[16] KRISTAN M,LEONARDIS A,MATAS J,et al. The sixth visual object tracking VOT2018 challenge results[C]//Proceedings of the 2018 European Conference on Computer Vision,LNCS 11129. Cham:Springer,2018:3-53.
[17] WANG Q,ZHANG L,BERTINETTO L,et al. Fast online object tracking and segmentation:a unifying approach[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:1328-1338.
[18] PINHEIRO P O,COLLOBERT R,DOLLÁR P,et al. Learning to segment object candidates[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge:MIT Press,2015:1990-1998.
[19] CHEN B X,TSOTSOS J K. Fast visual object tracking using ellipse fitting for rotated bounding boxes[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops. Piscataway:IEEE,2019:2281-2289.
[20] FITZGIBBON A W,PILU M,FISHER R B. Direct least squares fitting of ellipses[C]//Proceedings of the 13th International Conference on Pattern Recognition,Piscataway:IEEE,1996:253-257.
[21] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:770-778.
[22] WU Y,LIM J,YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9):1834-1848.
[23] LI B,WU W,WANG Q,et al. SiamRPN++:evolution of Siamese visual tracking with very deep networks[C]//Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2019:4277-4286.
[24] DANELLJAN M, HAGER G, KHAN F, et al. Convolutional features for correlation filter based visual tracking[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop. Washington, DC:IEEE Computer Society, 2015:621-629.
[25] BERTINETTO L,VALMADRE J,GOLODETZ S,et al. Staple:complementary learners for Real-Time Tracking[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:1401-1409.
[26] QI Y,ZHANG S,QIN L,et al. Hedged deep tracking[C]//Proceedings of the2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:4303-4311.
[27] SEUNGHOON H, TACKGEUN Y, SUHA K, et al. Online Tracking by Learning Discriminative Saliency Map with Convolutional neural network[C]//Proceedings of the 32nd International Conference on Machine Learning.[s. l.]:JMLR. org,2015:597-606.
[28] DANELLJAN M,HAGER G,KHAN F S, et al.[C] Discriminative scale space tracking[J]//IEEE transactions on pattern analysis and machine intelligence,2016, 39(8):1561-1575.

Accurate object tracking algorithm based on distance weighting overlap prediction and ellipse fitting optimization

基于距离加权重叠度估计与椭圆拟合优化的精确目标跟踪算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	XIONG Changzhen, LI Yan. Dynamic weighted siamese network tracking algorithm [J]. Journal of Computer Applications, 2020, 40(8): 2214-2218.
[2]	LI Shengwu, ZHANG Xuande. Multi-domain convolutional neural network based on self-attention mechanism for visual tracking [J]. Journal of Computer Applications, 2020, 40(8): 2219-2224.
[3]	FEI Dasheng, SONG Huihui, ZHANG Kaihua. Multi-level feature enhancement for real-time visual tracking [J]. Journal of Computer Applications, 2020, 40(11): 3300-3305.
[4]	JIANG Sihao, SONG Huihui, ZHANG Kaihua, TANG Runfa. Video object segmentation method based on dual pyramid network [J]. Journal of Computer Applications, 2019, 39(8): 2242-2246.
[5]	YANG Kang, SONG Huihui, ZHANG Kaihua. Real-time visual tracking based on dual attention siamese network [J]. Journal of Computer Applications, 2019, 39(6): 1652-1656.
[6]	LU Mingchi, WANG Shouhua, LI Yunke, JI Yuanfa, SUN Xiyan, DENG Guihui. Bluetooth location algorithm based on feature matching and distance weighting [J]. Journal of Computer Applications, 2018, 38(8): 2359-2364.
[7]	FAN Jiaqing, SONG Huihui, ZHANG Kaihua. Real-time visual tracking algorithm via channel stability weighted complementary learning [J]. Journal of Computer Applications, 2018, 38(6): 1751-1754.
[8]	CAO Junli, LI Jufeng. Improved ellipse fitting algorithm based on Letts criterion [J]. Journal of Computer Applications, 2017, 37(1): 273-277.
[9]	HU Zhaohua, XING Weiguo, HE Jun, ZHANG Xiuzai. Real-time object tracking method based on multi-channel kernel correlation filter [J]. Journal of Computer Applications, 2015, 35(12): 3544-3549.
[10]	. Moving object segmentation algorithm for river surveillance video [J]. Journal of Computer Applications, 2011, 31(01): 263-265.
[11]	. Feature extraction of self-adaptive illumination varying based on fuzzy control [J]. Journal of Computer Applications, 2010, 30(9): 2438-2440.
[12]	. Video object segmentation in H.264 compressed domain based on entropy energy [J]. Journal of Computer Applications, 2010, 30(12): 3265-3268.
[13]	. Moving targets detection using local OGHM [J]. Journal of Computer Applications, 2009, 29(3): 736-738.
[14]	Sen Guo Wei Liu JiangHua Wang. Touched human object segmentation based on Mean-shift algorithm [J]. Journal of Computer Applications, 2009, 29(1): 51-53.
[15]	Lu WANG YANG Lin-yun ZHUO Qing WANG Wen-yuan. Illumination robust Mean Shift tracking [J]. Journal of Computer Applications, 2008, 28(7): 1672-1674.