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Online Video Object Detection via Local and Mid-Range Feature Propagation

This work proposes a new Local and Mid-range feature Propagation (LMP) method for video object detection to well capture feature correlations and reduce the redundant computation. Specifically, the proposed LMP model contains two modules with two individual propagation schemes. The local module is leveraged to propagate motion and appearance context in short term. The local module is a lightweight one to greatly reduce the redundant computation without considering local attention. On the other hand, to explore the feature correlations in long term, the mid-range module based on the non-local attention mechanism is introduced by capturing relative longer-range relationships. By incorporating these two modules, LMP enables to enrich feature representation with fast computation. The proposed method is evaluated on the ImageNet VID dataset. The proposed LMP method achieves 64.2% mAP score at speed of 28.5 FPS on desktop GPUs, which is the state-of-the-art performance among one-stage MobileNet based detectors. Source code is available at https://github.com/ktw361/Local-Mid-Propagation.