  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.
