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.

中文翻译：

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用于视频对象检测的稳健高效的后处理

视频中的物体识别是许多应用的重要任务，包括自动驾驶感知、监控任务、可穿戴设备或物联网网络。由于模糊、遮挡或罕见的物体姿势，使用视频数据进行物体识别比使用静止图像更具挑战性。具有高计算成本的特定视频检测器或标准图像检测器与快速后处理算法一起实现了当前最先进的技术。这项工作引入了一种新颖的后处理管道，通过在跨帧检测之间引入基于学习的相似性评估，克服了先前后处理方法的一些局限性。我们的方法改进了最先进的特定视频检测器的结果，特别是关于快速移动的物体，并且资源需求低。