  Tracking people in a video sequence is one of the fields of interest in
computer vision. It has broad applications in motion capture and surveillance.
However, due to the complexity of human dynamic structure, detecting and
tracking are not straightforward. Consequently, different detection and
tracking techniques with different applications and performance have been
developed. To minimize the noise between the prediction and measurement during
tracking, Kalman filter has been used as a filtering technique. At the same
time, in most cases, detection and tracking results from a single sensor is not
enough to detect and track a person. To avoid this problem, using a
multi-sensor fusion technique is indispensable. In this paper, a comparative
survey of detection, tracking and multi-sensor fusion methods are presented.
