Object recognition in video is an important task for plenty of applications,
including autonomous driving perception, surveillance tasks, wearable devices
or IoT networks. Object recognition using video data is more challenging than
using still images due to blur, occlusions or rare object poses. Specific video
detectors with high computational cost or standard image detectors together
with a fast post-processing algorithm achieve the current state-of-the-art.
This work introduces a novel post-processing pipeline that overcomes some of
the limitations of previous post-processing methods by introducing a
learning-based similarity evaluation between detections across frames. Our
method improves the results of state-of-the-art specific video detectors,
specially regarding fast moving objects, and presents low resource
requirements. And applied to efficient still image detectors, such as YOLO,
provides comparable results to much more computationally intensive detectors.

本文提出了一种基于学习相似度评估的后处理方法，它可以克服先前后处理方法的一些局限性，提高特定视频检测器的结果，特别是在快速运动物体方面，并具有低资源要求。该方法还可以应用于像 YOLO 这样的高效静态图像检测器，提供与计算量更大的检测器相当的结果。