  Multiple object tracking has been a challenging field, mainly due to noisy
detection sets and identity switch caused by occlusion and similar appearance
among nearby targets. Previous works rely on appearance models built on
individual or several selected frames for the comparison of features, but they
cannot encode long-term appearance changes caused by pose, viewing angle and
lighting conditions. In this work, we propose an adaptive model that learns
online a relatively long-term appearance change of each target. The proposed
model is compatible with any feature of fixed dimension or their combination,
whose learning rates are dynamically controlled by adaptive update and spatial
weighting schemes. To handle occlusion and nearby objects sharing similar
appearance, we also design cross-matching and re-identification schemes based
on the application of the proposed adaptive appearance models. Additionally,
the 3D geometry information is effectively incorporated in our formulation for
data association. The proposed method outperforms all the state-of-the-art on
the MOTChallenge 3D benchmark and achieves real-time computation with only a
standard desktop CPU. It has also shown superior performance over the
state-of-the-art on the 2D benchmark of MOTChallenge.
