  We propose in this paper a tracking algorithm which is able to adapt itself
to different scene contexts. A feature pool is used to compute the matching
score between two detected objects. This feature pool includes 2D, 3D
displacement distances, 2D sizes, color histogram, histogram of oriented
gradient (HOG), color covariance and dominant color. An offline learning
process is proposed to search for useful features and to estimate their weights
for each context. In the online tracking process, a temporal window is defined
to establish the links between the detected objects. This enables to find the
object trajectories even if the objects are misdetected in some frames. A
trajectory filter is proposed to remove noisy trajectories. Experimentation on
different contexts is shown. The proposed tracker has been tested in videos
belonging to three public datasets and to the Caretaker European project. The
experimental results prove the effect of the proposed feature weight learning,
and the robustness of the proposed tracker compared to some methods in the
state of the art. The contributions of our approach over the state of the art
trackers are: (i) a robust tracking algorithm based on a feature pool, (ii) a
supervised learning scheme to learn feature weights for each context, (iii) a
new method to quantify the reliability of HOG descriptor, (iv) a combination of
color covariance and dominant color features with spatial pyramid distance to
manage the case of object occlusion.
