Part‐MOT: A multi‐object tracking method with instance part‐based embedding
Xiaohu Liu, Yichuang Luo, Keding Yan, Jianfei Chen, Zhiyong Lei · IET Image Processing · 2021
Abstract Part‐MOT, a one‐stage anchor‐free architecture which unifies the object identification representation and detection in one task for visual object tracking is presented. For object representation, a position relevant feature is obtained using the center‐ness information, which takes advantage of the anchor‐free ideal to encode the feature map as the instance‐aware embedding. To adapt to the object's movement, the clustering‐based method to get the global instance feature is introduced. This enables this approach more robust to make better tracking decisions. Part‐MOT achieves the state‐of‐the‐art performance on public datasets, with especially strong results for object deformation and movement changes.