Title

Previewer For Multi-Scale Object Detector

Keywords

Convolutional Neural Networks; Object Detection; Receptive Field

Abstract

Most multi-scale detectors face a challenge of small-size false positives due to the inadequacy of low-level features, which have small receptive field sizes and weak semantic capabilities. This paper demonstrates independent predictions from different feature layers on the same region is beneficial for reducing false positives. We propose a novel light-weight previewer block, which previews the objectness probability for the potential regression region of each prior box, using the stronger features with larger receptive fields and more contextual information for better predictions. This previewer block is generic and can be easily implemented in multi-scale detectors, such as SSD, RFBNet and MS-CNN. Extensive experiments are conducted on PASCAL VOC and KITTI pedestrian benchmark to show the superiority of the proposed method.

Publication Date

10-15-2018

Publication Title

MM 2018 - Proceedings of the 2018 ACM Multimedia Conference

Number of Pages

265-273

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3240508.3240544

Socpus ID

85058240499 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85058240499

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