  In this paper we propose a novel approach for detecting and tracking objects
in videos with variable background i.e. videos captured by moving cameras
without any additional sensor. In a video captured by a moving camera, both the
background and foreground are changing in each frame of the image sequence. So
for these videos, modeling a single background with traditional background
modeling methods is infeasible and thus the detection of actual moving object
in a variable background is a challenging task. To detect actual moving object
in this work, spatio-temporal blobs have been generated in each frame by
spatio-temporal analysis of the image sequence using a three-dimensional Gabor
filter. Then individual blobs, which are parts of one object are merged using
Minimum Spanning Tree to form the moving object in the variable background. The
height, width and four-bin gray-value histogram of the object are calculated as
its features and an object is tracked in each frame using these features to
generate the trajectories of the object through the video sequence. In this
work, problem of data association during tracking is solved by Linear
Assignment Problem and occlusion is handled by the application of kalman
filter. The major advantage of our method over most of the existing tracking
algorithms is that, the proposed method does not require initialization in the
first frame or training on sample data to perform. Performance of the algorithm
has been tested on benchmark videos and very satisfactory result has been
achieved. The performance of the algorithm is also comparable and superior with
respect to some benchmark algorithms.
