  Despite the recent success of video object detection on Desktop GPUs, its
architecture is still far too heavy for mobiles. It is also unclear whether the
key principles of sparse feature propagation and multi-frame feature
aggregation apply at very limited computational resources. In this paper, we
present a light weight network architecture for video object detection on
mobiles. Light weight image object detector is applied on sparse key frames. A
very small network, Light Flow, is designed for establishing correspondence
across frames. A flow-guided GRU module is designed to effectively aggregate
features on key frames. For non-key frames, sparse feature propagation is
performed. The whole network can be trained end-to-end. The proposed system
achieves 60.2% mAP score at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8).
