This thesis is mainly targeted at self-adaptation adjustment in the search region: at first, design a staging predation space self-adaptation scale strategy bat algorithm (AP-RBA), and then, use AP-RBA algorithm to establish a target tracking strategy of optimized particle filter which can effectively solve two kinds of problems: (1) particle impoverishment phenomena produced in particle filter; (2) effective tracking targets based on few particles, thus simplifying complexity of particle filter, and then, adopt the criterion weight strategy to achieve maximum a posteriori and change of criterion weight to realize effective improvement of particle distribution and promote efficiency of particle filter process.
This is a preview of subscription content, log in to check access.
Buy single article
Instant unlimited access to the full article PDF.
Price includes VAT for USA
Afolabi, R.O., Dadlani, A., and Kim, K., Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A Survey, IEEE Commun. Surv. Tutorials, 2013, vol. 15, no. 1, pp. 240–254.
Mazomenos, E.B., Biswas, D., and Acharyya, A., A low-complexity ECG feature extraction algorithm for mobile healthcare applications, IEEE J. Biomed. Health Inf., 2013, vol. 17, no. 2, pp. 459–469.
Berger, C., Voltersen, M., and Eckardt, R., Multi-modal and multi-temporal data fusion: Outcome of the 2012 GRSS Data Fusion Contest, IEEE J. Sel. Top. Appl. Earth Obs. & Remote Sens., 2013, vol. 6, no. 3, pp. 1324–1340.
Chicca, E., Stefanini, F., and Bartolozzi, C., Neuromorphic electronic circuits for building autonomous cognitive systems, Proc. IEEE, 2014, vol. 102, no. 9, pp. 1367–1388.
Ok, A.O., Senaras, C., and Yuksel, B., Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery, IEEE Trans. Geosci. Remote Sens., 2013, vol. 51, no. 3, pp. 1701–1717.
Laurin, G.V., Liesenberg, V., and Chen, Q., Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa, Int. J. Appl. Earth Obs. Geoinf., 2013, vol. 21, no. 4, pp. 7–16.
Adamson, P.B., Abraham, W.T., and Bourge, R.C., Wireless pulmonary artery pressure monitoring guides management to reduce decompensation in heart failure with preserved ejection fraction, Circ.: Heart Failure, 2014, vol. 7, no. 6, pp. 976–977.
Romano, P. and Paolone, M., Enhanced interpolated-DFT for synchrophasor estimation in FPGAs: Theory, implementation, and validation of a PMU prototype, IEEE Trans. Instrum. Meas., 2014, vol. 63, no. 12, pp. 2824–2836.
Knopf, A., Nill, S., and Yohannes, I., Challenges of radiotherapy: Report on the 4D treatment planning workshop 2013, Phys. Med., 2014, vol. 30, no. 7, pp. 809–815.
Berta, R., Bellotti, F., and De Gloria, A., Electroencephalogram and physiological signal analysis for assessing flow in games, IEEE Trans. Comput. Intell. AI Games, 2013, vol. 5, no. 2, pp. 164–175.
Jo, J., Lee, S.J., and Kang, R.P., Detecting driver drowsiness using feature-level fusion and user-specific classification, Expert Syst. Appl., 2014, vol. 41, no. 4, pp. 1139–1152.
Tome, P., Fierrez, J., and Vera-Rodriguez, R., Soft biometrics and their application in person recognition at a distance, IEEE Trans. Inf. Forensics Secur., 2014, vol. 9, no. 3, pp. 464–475.
Akimoto, M., Nakamura, M., and Mukumoto, N., Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT, Med. Phys., 2013, vol. 40, no. 9, 091 705.
Gum, J.L., Glassman, S.D., and Carreon, L.Y., Is type of compensation a predictor of outcome after lumbar fusion, Spine, 2013, vol. 38, no. 5, pp. 443–448.
Avidan, S., Ensemble tracking, IEEE Trans. Pattern Anal. Mach. Intell., 2012, vol. 29, no. 2, pp. 261–271.
Avidan, S., Support vector tracking, IEEE Trans. Pattern Anal. Mach. Intell., 2004, vol. 26, no. 8, pp. 1064–1072.
Babu, R., Ramakrishnan, K., and Srinivasan, S., Video object segmentation: A compressed domain approach, IEEE Trans. Circuits Syst. Video Technol., 2004, vol. 14, no. 4, pp. 462–474.
Isard, M. and Blake, A., ICONDENSATION: Unifying low level and high-level tracking in a stochastic framework, Lect. Notes Comput. Sci., 1998, vol. 1406.
The authors declare that they have no conflicts of interest.
About this article
Cite this article
Yingying Feng, Zhao, S. & Liu, H. Target Tracking Based on Multi Feature Selection Fusion Compensation in Monitoring Video. Aut. Control Comp. Sci. 53, 522–531 (2019). https://doi.org/10.3103/S0146411619060051
- feature selection
- video monitoring
- target tracking
- moving background
- image segmentation