Pedestrian detection with super-resolution reconstruction for low-quality image

Highlights：

• To verify the effectiveness of the proposed SRD algorithm, a new low-quality playground (PG) dataset for pedestrian detection is collected that provides dense and occluded pedestrians with light interference and motion blur in the surveillance images.

• Compared with the state-of-the-art methods, our proposed SRD method achieves higher accuracy of pedestrian detection based on the PG dataset. In particular, we demonstrate improved results for more difficult detection cases (light interference and occluded), and overall higher localization precision.

摘要

•This paper proposes a new end-to-end pedestrian detection method called the super-resolution detection (SRD) network that aims to solve the low-quality and occlusion problems in intelligent video surveillance.•To verify the effectiveness of the proposed SRD algorithm, a new low-quality playground (PG) dataset for pedestrian detection is collected that provides dense and occluded pedestrians with light interference and motion blur in the surveillance images.•Compared with the state-of-the-art methods, our proposed SRD method achieves higher accuracy of pedestrian detection based on the PG dataset. In particular, we demonstrate improved results for more difficult detection cases (light interference and occluded), and overall higher localization precision.

论文评审过程：Received 15 July 2020, Revised 6 December 2020, Accepted 2 January 2021, Available online 14 February 2021, Version of Record 20 February 2021.