Robust Visual Tracking Using Illumination Invariant Features in Adaptive Scale Model
Keywords:Visual Tracking, Correlation Based Filtering, Adaptive Scale Space, Locality Sensitive Histrogram
When entering into the realm of Computer Vision, the first thing which comes in to mind is Visual tracking. Visual tracking by far comes into one of the most actively investigated research areas because of the fact that it has an extensive collection of applications in areas such as activity recognition, surveillance, motion analysis and as well as human computer interaction. Some serious challenges of this area which still create hindrance in achieving 100% accuracy are abrupt appearance and pose changes of an object along with its background blockage due to blockages called occlusion, illumination and lighting variances and changes in scale of target object in the frames. Moreover, diverse algorithms had been proposed for the resolution of said issue. Now in such cases, if we study the statistical analysis of correlation between two frames in a certain video, it can be efficiently utilized to get the most exact location of the targeted object. The algorithms in existence today do not completely exploit a strong spatio-temporal relationship that very often occurs between the two successive frames in a video sequence. Recent advances in correlation-based tracking systems have been proposed to address the problem in successive frames. In this thesis a very simple yet quite speedy and robust algorithm that in actual brings all the relevant information used for Visual Tracking. Two of the Models proposed are the “Locality Sensitive Histogram” and “Discriminative Scale Tracking Method”. These are robust enough to the variations which are based on appearance which are normally presented by blockage, pose, illumination and lighting variations alike. A scheme is proposed called scale adaptation which is very much clever to adapt variations of targeted scale in the most efficient manner. The Discriminative Scale Tracking Method is used for detection as well as scale change ultimately resulting in an effective tracking method in the end. Various different experiments with the best algorithms have demonstrated on challenging sequences that the suggested methodology attains promising results as far as robustness, accuracy, and speed is concerned.
Maggio, Emilio, and Andrea Cavallaro. “Video tracking: theory and practice”, John Wiley & Sons, 2011.
Vapnik, Vladimir, “Statistical learning theory”, 1998, Wiley, New York.
A. Yilmaz, O. Javed, and M. Shah. “Object Tracking: A survey”, ACM Computing Surveys, 2006.
Y. Andrew and M. Jordan, “On Discriminative vs. Generative Classifiers: A comparison of logistic Regression and Naive Bayes”, Neural Information Processing Systems, 2001.
H. Shengfeng, Y. Qingxiong, L. Rynson, J. Wang and M. Yang, “Visual Tracking via Locality Sensitive Histograms”, 2013, Computer Vision and Pattern Recognition, June.
T. Dinh, N. Vo, and G. Medioni, “Context Tracker: Exploring supporters and distracters in unconstrained Environments”, 2011, European Conference on Computer Vision.
Yi Wu ,Jongwoo Lim and Ming-Hsuan Yang, “Online Object Tracking: A Benchmark ”, 2013, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
M. Danelljan, G. Häger, F. Shahbaz Khan, and M. Felsberg. “Accurate scale estimation for robust visual tracking”,2014, In Proceedings of the British Machine Vision Conference (BMVC).
M. Danelljan, A. Robinson, Fahad Khan and M. Felsberg, “Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking”, 2016,In Proceedings of the European Conference on Computer Vision (ECCV).
V. Boddeti, T. Kanade, and B. Kumar, 2013, “Correlation filters for object alignment” , IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
H. Bolme, R. Beveridge, A. Draper and Yui Man Lui“Visual Object Tracking using Adaptive Correlation Filters”, 2011, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
H. shengfeng, Y. Qing, L.Rynson and Yang Ming“Visual Tracking via Locality Sensitive Histograms”, 2013, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
M. Danelljan, H. Gustav, Fahad Khan and M. Felsberg, “Discriminative Scale Space Tracking”, 2017 ,in Proceedings of the IEEE Transcations on Pattern Anslysis and Machine Intelligence (PAMI).
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Copyright (c) 2020 Muhammad Muazzam Hussain, Kashif Faheem, Arslan Majid
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The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.