Flow-Guided Feature Aggregation for Video Object Detection

TL;DR

This paper introduces a flow-guided feature aggregation method for video object detection that enhances per-frame features by leveraging temporal coherence, significantly improving accuracy especially for fast-moving objects.

Contribution

It presents an end-to-end trainable framework that aggregates features along motion paths, outperforming previous box-level methods and matching top challenge-winning systems.

Findings

Significant accuracy improvement over single-frame baselines.

Achieved top performance in ImageNet VID challenges 2016 and 2017.

Effective for fast-moving and challenging objects.

Abstract

Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flow-guided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID, especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The code is available at https://github.com/msracver/Flow-Guided-Feature-Aggregation.