  Compared with still image object detection, video object detection (VOD)
needs to particularly concern the high across-frame variation in object
appearance, and the diverse deterioration in some frames. In principle, the
detection in a certain frame of a video can benefit from information in other
frames. Thus, how to effectively aggregate features across different frames is
key to the target problem. Most of contemporary aggregation methods are
tailored for two-stage detectors, suffering from high computational costs due
to the dual-stage nature. On the other hand, although one-stage detectors have
made continuous progress in handling static images, their applicability to VOD
lacks sufficient exploration. To tackle the above issues, this study invents a
very simple yet potent strategy of feature selection and aggregation, gaining
significant accuracy at marginal computational expense. Concretely, for cutting
the massive computation and memory consumption from the dense prediction
characteristic of one-stage object detectors, we first condense candidate
features from dense prediction maps. Then, the relationship between a target
frame and its reference frames is evaluated to guide the aggregation.
Comprehensive experiments and ablation studies are conducted to validate the
efficacy of our design, and showcase its advantage over other cutting-edge VOD
methods in both effectiveness and efficiency. Notably, our model reaches
\emph{a new record performance, i.e., 92.9\% AP50 at over 30 FPS on the
ImageNet VID dataset on a single 3090 GPU}, making it a compelling option for
large-scale or real-time applications. The implementation is simple, and
accessible at \url{https://github.com/YuHengsss/YOLOV}.
