Similar articles

Abstract: Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

基于可靠特征点分配算法的鲁棒性跟踪框架

[1]Bay, H., Ess, A., Tuytelaars, T., et al., 2008. Speeded-up robust features (SURF). Comput. Vis. Image Understand., 110(3):346-359.

[2]Brox, T., Bruhn, A., Papenberg, N., et al., 2004. High accuracy optical flow estimation based on a theory for warping. European Conf. on Computer Vision, p.25-36.

[3]Cheng, C.W., Ou, W.L., Fan, C.P., 2016. Fast ellipse fitting based pupil tracking design for human-computer interaction applications. IEEE Int. Conf. on Consumer Electronics, p.445-446.

[4]Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.886-893.

[5]Elhamod, M., Levine, M.D., 2013. Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans. Intell. Transp. Syst., 14(2): 688-699.

[6]Elmenreich, W., Koplin, M.A., 2011. Time-triggered object tracking subsystem for advanced driver assistance systems. Elektrotechn. Inform., 128(6):203-208.

[7]Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Prentice Hall, Inc., New Jersey.

[8]Harris, C., Stephens, M., 1988. A combined corner and edge detector. Proc. Alvey Vision Conf., p.147-151.

[9]Henriques, J.F., Caseiro, R., Martins, P., et al., 2015. High-speed tracking with kernelized correlation filters. IEEE Trans. Patt. Anal. Mach. Intell., 37(3):583-596.

[10]Jeong, J.M., Yoon, T.S., Park, J.B., 2014. Kalman filter based multiple objects detection-tracking algorithm robust to occlusion. Proc. SICE Annual Conf., p.941-946.

[11]Jia, C.X., Wang, Z.L., Wu, X., et al., 2015. A tracking-learning-detection (TLD) method with local binary pattern improved. IEEE Int. Conf. on Robotics and Biomimetics, p.1625-1630.

[12]Jung, Y., Yoon, Y., 2015. Behavior tracking model in dynamic situation using the risk ratio EM. Int. Conf. on Information Networking, p.444-448.

[13]Kalal, Z., Mikolajczyk, K., Matas, J., 2010a. Forward-backward error: automatic detection of tracking failures. 20th Int. Conf. on Pattern Recognition, p.23-26.

[14]Kalal, Z., Matas, J., Mikolajczyk, K., 2010b. P-N learning: bootstrapping binary classifiers by structural constraints. IEEE Conf. on Computer Vision and Pattern Recognition, 49-56.

[15]Kalal, Z., Mikolajczyk, K., Matas, J., 2012. Tracking-learning-detection. IEEE Trans. Patt. Anal. Mach. Intell., 34(7):1409-1422.

[16]Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. J. Basic Eng., 82(1):35-45.

[17]Kaur, H., Sahambi, J.S., 2015. Vehicle tracking using fractional order Kalman filter for non-linear system. Int. Conf. on Computing, Communication and Automation, p.474-479.

[18]Kong, H., Akakin, H.C., Sarma, S.E., 2013. A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans. Cybern., 43(6):1719-1733.

[19]Li, Y., Zhu, J.K., Hoi, S.C.H., 2015. Reliable patch trackers: robust visual tracking by exploiting reliable patches. IEEE Conf. on Computer Vision and Pattern Recognition, p.353-361.

[20]Liu, S., Zhang, T.Z., Cao, X.C., et al., 2016. Structural correlation filter for robust visual tracking. IEEE Conf. on Computer Vision and Pattern Recognition, p.4312-4320.

[21]Liu, T., Wang, G., Yang, Q.X., 2015. Real-time part-based visual tracking via adaptive correlation filters. IEEE Conf. on Computer Vision and Pattern Recognition, p.4902-4912.

[22]Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 60(2):91-110.

[23]Ning, G.H., Zhang, Z., Huang, C., et al., 2016. Spatially supervised recurrent convolutional neural networks for visual object tracking. arXiv:1607.05781v1.

[24]Prakash, U.M., Thamaraiselvi, V.G., 2014. Detecting and tracking of multiple moving objects for intelligent video surveillance systems. 2nd Int. Conf. on Current Trends in Engineering and Technology, p.253-257.

[25]Redmon, J., Divvala, S., Girshick, R., et al., 2016. You only look once: unified, real-time object detection. IEEE Conf. on Computer Vision and Pattern Recognition, p.779-788.

[26]Sun, X., Yao, H.X., Zhang, S.P., 2010. A refined particle filter method for contour tracking. SPIE, 7744:77441M.

[27]Tarkov, M.S., Dubynin, S.V., 2013. Real-time object tracking by CUDA-accelerated neural network. J. Comput. Sci. Appl., 1(1):1-4.

[28]Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.511-518.

[29]Xu, F., Gao, M., 2010. Human detection and tracking based on HOG and particle filter. 3rd Int. Congress on Image and Signal Processing, p.1503-1507.

[30]Yu, H.M., Zeng, X., 2015. Visual tracking combined with ranking vector SVM. J. Zhejiang Univ. (Eng. Sci.), 49(6): 1015-1021 (in Chinese).

[31]Yu, W.S., Tian, X.H., Hou, Z.Q., et al., 2015. Multi-scale mean shift tracking. IET Comput. Vis., 9(1):110-123.

[32]Zhang, R.F., Xiao, H.H., Deng, T., et al., 2016. A robust point detection algorithm based on wavelet transform for visual tracking. Int. Congress on Image and Signal Processing, Biomedical Engineering and Informatics, p.1-5.