Abstract:
It is very easy for humans to recognize the object and to follow it till their sight range,
but with the advancement of technology, we want to take this work through machines
so that we can get better results according to our desire. So, with this need, the
computer vision field came out of the box by using its sub-fields like object detection
and visual object tracking. Tremendous efforts are being done by researchers in a field
of object tracking, but it is still open to be explored because the challenges of visual
object tracking still exist and this thesis also deals with the visual object tracking
challenges. We consider the main challenge of tracking by our motivation i.e.,
Occlusion and illumination variation. So, in this regard, we select the state of the art
algorithm name with “Adaptive Correlation Filters with Long-Term and Short-Term
Memory for Object Tracking” in which three correlation filters were proposed 1) LongTerm Filter 2) Translation Filter 3) Scale filter. These filters work outstanding in most
of the video sequence, but we have found that their performance degrades for some of
the video sequences bearing challenges of occlusion and illumination variation. In
order to solve these problems, we incorporate one more filter that is Kalman filter to
the algorithm; enhanced algorithm yields better results as compared to its counterpart
method when video sequences having challenges of occlusion and illumination
variation is given to the proposed method has been tested on standard dataset i.e.,
Object Tracking Benchmark 13 containing 49 video sequences with different challenges
the comparison of the proposed method with its base method i.e., existing selected
method. The proposed method highlights its effectiveness both quantitatively and
qualitatively, especially in occluded and varying illumination environment.
