3 years ago

The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection.

Lu Zhang, Zhiyong Liu, Xiangyu Chen, Xu Yang

Aggregating extra features of novel modality brings great advantages for building robust pedestrian detector under adverse illumination conditions. However, misaligned imagery still persists in multispectral scenario and will depress the performance of detector in a non-trivial way. In this paper, we first present and explore the cross-modality disparity problem in multispectral pedestrian detection, providing insights into the utilization of multimodal inputs. Then, to further address this issue, we propose a novel framework including a region feature alignment module and the region of interest (RoI) jittering training strategy. Moreover, dense, high-quality, and modality-independent color-thermal annotation pairs are provided to scrub the large-scale KAIST dataset to benefit future multispectral detection research. Extensive experiments demonstrate that the proposed approach improves the robustness of detector with a large margin and achieves state-of-the-art performance with high efficiency. Code and data will be publicly available.

Publisher URL: http://arxiv.org/abs/1901.02645

DOI: arXiv:1901.02645v1

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.