Abstract
Abstract: Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with highlevel context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which can learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures.
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