Object-aware bounding box regression for online multi-object tracking

Based on the detection technology, regressing predicted bounding boxes provides an effective approach in multiple object tracking. However, if only the information in the current frame is considered, identity (ID) switch is easy to happen when objects interact. In this paper, we propose an Object-Aware Bounding Box Regression (OABBR) for online multi-object tracking. We first propose an Object-Aware Spatial-Temporal Understanding (OASTU) module to mine the correlated information in corresponding object’s trajectory. OASTU updates features of predictions by the correlated information. By using the updated features, we further perform bounding box regression. Besides, to make features extracted by the backbone network contain more ID information, we construct a weak ID constraint in the training phase. The introduced weak ID constraint facilitates OASTU to be ID consistent and further alleviates ID switch. By exploring the spatial-temporal information in corresponding object’s trajectory, each prediction is able to know the information of the corresponding object, which makes the purpose of its regression clearer. Experimental results on four public benchmarks demonstrate the effectiveness of the proposed method.