Abstract:
In recent times, a lot of focus has been diverted to model free object tracking instead of
model based. Model free tracking requires no prior model of object of interest. The object
is represented by its location and size which is called initial annotation. The task of tracker
is to build a correspondence of this object in all consecutive frames. The tracker follows
this object and tries not to loose the track in whatever difficult situations like clutter, object
hiding or pose changes.
In this thesis, a novel and efficient object detection and tracking method is proposed keeping
in consideration the real time requirements. The combination of two features Local Binary
Pattern (LBP) and Histogram of Oriented Gradients (HOG) is applied for object localization.
Both of these feature complement each other and their combination when fed to a linear
Support Vector Machine (SVM) classifier would lead us to a robust learning measure.
Our main focus, in this study, is on decreasing the time required to process each frame. The
target object is represented by a parity based linear combination of HOG and LBP feature. A
robust multi-modal learning mechanism is used for a more discriminative hypothesis generation.
We have evaluated our approach with the widely available visual tracker benchmarking
videos and have found our technique to be working efficiently.
