  Visual-based perception is the key module for autonomous driving. Among those
visual perception tasks, video object detection is a primary yet challenging
one because of feature degradation caused by fast motion or multiple poses.
Current models usually aggregate features from the neighboring frames to
enhance the object representations for the task heads to generate more accurate
predictions. Though getting better performance, these methods rely on the
information from the future frames and suffer from high computational
complexity. Meanwhile, the aggregation process is not reconfigurable during the
inference time. These issues make most of the existing models infeasible for
online applications. To solve these problems, we introduce a stepwise spatial
global-local aggregation network. Our proposed models mainly contain three
parts: 1). Multi-stage stepwise network gradually refines the predictions and
object representations from the previous stage; 2). Spatial global-local
aggregation fuses the local information from the neighboring frames and global
semantics from the current frame to eliminate the feature degradation; 3).
Dynamic aggregation strategy stops the aggregation process early based on the
refinement results to remove redundancy and improve efficiency. Extensive
experiments on the ImageNet VID benchmark validate the effectiveness and
efficiency of our proposed models.
