Open Access Open Access  Restricted Access Subscription Access
Cover Image

Multi-Object Tracking Based on Hybrid Kalman/H∞ Filtering

Adinarayana Ekkurthi Ekkurthi, Dr.G. Sudhavani

Abstract


In this paper, we utilize the hybrid H-infinity and Kalman filtering for multiple object tracking in the video arrangements. Here, the proposed system will be the joined execution of Kalman filter and the H-infinity filter. The Kalman filter, known as the optimal linear quadratic estimator, it is a profoundly productive filter and it broadly utilized as a part of tracking frameworks. The H∞ filter is a recursive algorithm, just the last time step and current state estimation are required for object tracking yet no history perception is required. Consequently, there would be no necessity for a high limit of computational stockpiling. At first, the color spatial feature, texture feature and the edge gradient features are removed from the targeted object to figure the matching degree between the Candidate feature and the object feature. At that point the joined H-infinity and the Kalman filter approach is planned to find the certifiable location of the objects and additionally assess the speed of that object in light of the fact that the objects are moved by certain motion law (position and velocity). The Kalman filter doesn't limit the mean square error. In this way, the H-infinity filter limits the mean square error and also utilized to limit the impact of unexpected noise whose insights are obscure. Usage of the proposed model and the techniques were completed in MATLAB and the execution of the proposed strategy has better execution and precision.


Keywords


Multi-object tracking; Background subtraction; Histogram equalization; Multi feature fusion; Hybrid Kalman and H-infinity filter.

Full Text:

PDF

References


Amith, R., Manjunath Aradhya, V. N., 2016. A Hybrid Based Approach for Object Tracking in Video. IRA-International Journal of Technology & Engineering. 4(1), 13-23.

Arvanitidou, M. G., Tok, M., Glantz, A., Krutz, A., Sikora, T., 2013. Motion-based object segmentation using hysteresis and bidirectional inter-frame change detection in sequences with moving camera. Signal Processing: Image Communication. 28(10), 1420-1434.

Gül, S., Meyer, J. T., Hellge, C., Schierl, T., Samek, W., 2016. Hybrid video object tracking in H. 265/HEVC video streams. In Multimedia Signal Processing (MMSP), 2016 IEEE 18th International Workshop on, Montreal, QC, Canada, pp. 1-5

He, Z., Li, X., You, X., Tao, D., Tang, Y. Y., 2016. Connected component model for multi-object tracking. IEEE transactions on image processing. 25(8), 3698-3711.

Hu, M., Liu, Z., Zhang, J., Zhang, G. 2017. Robust object tracking via multi-cue fusion. Signal Processing. 139, 86-95.

Hu, W. C., Chen, C. H., Chen, C. M., Chen, T. Y., 2014. Effective moving object detection from videos captured by a moving camera. In Intelligent Data analysis and its Applications, Springer International Publishing., Switzerland, pp. 343-353.

Hu, W. C., Chen, C. H., Chen, T. Y., Huang, D. Y., Wu, Z. C., 2015. Moving object detection and tracking from video captured by moving camera. Journal of Visual Communication and Image Representation. 30, 164-180.

Hu, W., Li, W., Zhang, X., Maybank, S., 2015. Single and multiple object tracking using a multi-feature joint sparse representation. IEEE transactions on pattern analysis and machine intelligence. 37(4), 816-833.

Kanan, H. R., Karimi, P., 2012. Visual Object Tracking Using Fuzzy-based Thresholding and Kalman Filter. International Journal of Modeling and Optimization. 2(3), 227.

Karavasilis, V., Nikou, C., Likas, A., 2017. Real time visual tracking using a spatially weighted von Mises mixture model. Pattern Recognition Letters. 90, 50-57.

Kim, D. Y., Jeon, M., 2014. Data fusion of radar and image measurements for multi-object tracking via Kalman filtering. Information Sciences. 278, 641-652.

Kwon, J., & Lee, K. M. 2017. Adaptive Visual Tracking with Minimum Uncertainty Gap Estimation. IEEE transactions on pattern analysis and machine intelligence. 39(1), 18-31.

Li, H., Liu, Y., Wang, C., Zhang, S., Cui, X. 2016. Tracking algorithm of multiple pedestrians based on particle filters in video sequences. Computational intelligence and neuroscience. 2016, 13-30.

Li, H., Xiong, S., Duan, P., Kong, X., 2012. Multitarget tracking of pedestrians in video sequences based on particle filters. Advances in Multimedia. 2012, 1-14.

Liu, B., Feng, Z., Sang, A., Su, Y., Zhang, T., Min, F., 2013. Monocular 3D Hand Tracking Using Particle Filter with Partitioned Sampling. Journal Of Information &Computational Science. 10(15), 4733-4742.

Liu, H., Wu, W., 2017. Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking. Sensors. 17(4), 741.

Markovic, I., Petrovic, I., (2014). Bayesian Sensor Fusion Methods for Dynamic Object Tracking—A Comparative Study. Automatika. 55(4), 386-398.

Milan, A., Schindler, K., Roth, S., 2016. Multi-target tracking by discrete-continuous energy minimization. IEEE transactions on pattern analysis and machine intelligence. 38(10), 2054-2068.

Naiel, M. A., Ahmad, M. O., Swamy, M. N. S., Lim, J., Yang, M. H., 2017. Online multi-object tracking via robust collaborative model and sample selection. Computer Vision and Image Understanding. 154, 94-107.

Qi, B., Ghazal, M., Amer, A., 2008. Robust global motion estimation oriented to video object segmentation. IEEE Transactions on Image Processing. 17(6), 958-967.

Reuter, S., Wilking, B., Wiest, J., Munz, M., Dietmayer, K., 2013. Real-time multi-object tracking using random finite sets. IEEE Transactions on Aerospace and Electronic Systems. 49(4), 2666-2678.

Saeed, S. Z., Abdilatef, M. A., Yosif, Z. M. 2016. Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization. International Journal of Computer Applications. 146(4), 43-50.

Shi, P., Zhang, Y., Chadli, M., & Agarwal, R. K., 2016. Mixed H-infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays. IEEE transactions on neural networks and learning systems. 27(4), 903-909.

Sui, Y., Zhang, L., 2015. Visual tracking via locally structured Gaussian process regression. IEEE Signal Processing Letters. 22(9), 1331-1335.

Truong, M. T. N., Kim, S., 2017. Parallel implementation of color-based particle filter for object tracking in embedded systems. Human-centric Computing and Information Sciences. 7(1), 2.

Walia, G. S., Raza, S., Gupta, A., Asthana, R., & Singh, K., 2017. A novel approach of multi-stage tracking for precise localization of target in video sequences. Expert Systems with Applications. 78, 208-224.

Wang, X., Zhang, H., Jiang, X., Yang, Y., 2011. Target tracking based on the extended H-infinity filter in wireless sensor networks. Journal of Control Theory and Applications. 9(4), 479-486.

Yu, R., Cheng, I., Zhu, B., Bedmutha, S., Basu, A., 2017. Adaptive Resolution Optimization and Tracklet Reliability Assessment for Efficient Multi-Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology. PP(99), 1-1.

Zhang, T., Ghanem, B., Liu, S., Ahuja, N., 2013. Robust visual tracking via structured multi-task sparse learning. International journal of computer vision. 101(2), 367-383.

Zhong, W., Lu, H., Yang, M. H., 2014. Robust object tracking via sparse collaborative appearance model. IEEE Transactions on Image Processing. 23(5), 2356-2368.

Zhou, T., He, X., Xie, K., Fu, K., Zhang, J., Yang, J., 2015. Robust visual tracking via efficient manifold ranking with low-dimensional compressive features. Pattern Recognition. 48(8), 2459-2473.


Refbacks

  • There are currently no refbacks.