  For multi-target tracking, target representation plays a crucial rule in
performance. State-of-the-art approaches rely on the deep learning-based visual
representation that gives an optimal performance at the cost of high
computational complexity. In this paper, we come up with a simple yet effective
target representation for human tracking. Our inspiration comes from the fact
that the human body goes through severe deformation and inter/intra occlusion
over the passage of time. So, instead of tracking the whole body part, a
relative rigid organ tracking is selected for tracking the human over an
extended period of time. Hence, we followed the tracking-by-detection paradigm
and generated the target hypothesis of only the spatial locations of heads in
every frame. After the localization of head location, a Kalman filter with a
constant velocity motion model is instantiated for each target that follows the
temporal evolution of the targets in the scene. For associating the targets in
the consecutive frames, combinatorial optimization is used that associates the
corresponding targets in a greedy fashion. Qualitative results are evaluated on
four challenging video surveillance dataset and promising results has been
achieved.
