  In this work, we introduce a novel tracker designed for online multiple
object tracking with a focus on being simple, while being effective. we provide
multiple feature modules each of which stands for a particular appearance
information. By integrating distinct appearance features, including clothing
color, style, and target direction, alongside a ReID network for robust
embedding extraction, our tracker significantly enhances online tracking
accuracy. Additionally, we propose the incorporation of a stronger detector and
also provide an advanced post processing methods that further elevate the
tracker's performance. During real time operation, we establish measurement to
track associated distance function which includes the IoU, direction, color,
style, and ReID features similarity information, where each metric is
calculated separately. With the design of our feature related distance
function, it is possible to track objects through longer period of occlusions,
while keeping the number of identity switches comparatively low. Extensive
experimental evaluation demonstrates notable improvement in tracking accuracy
and reliability, as evidenced by reduced identity switches and enhanced
occlusion handling. These advancements not only contribute to the state of the
art in object tracking but also open new avenues for future research and
practical applications demanding high precision and reliability.
