41,136 research outputs found
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
The goal of low-light image enhancement is to restore the color and details
of the image and is of great significance for high-level visual tasks in
autonomous driving. However, it is difficult to restore the lost details in the
dark area by relying only on the RGB domain. In this paper we introduce
frequency as a new clue into the network and propose a novel DCT-driven
enhancement transformer (DEFormer). First, we propose a learnable frequency
branch (LFB) for frequency enhancement contains DCT processing and
curvature-based frequency enhancement (CFE). CFE calculates the curvature of
each channel to represent the detail richness of different frequency bands,
then we divides the frequency features, which focuses on frequency bands with
richer textures. In addition, we propose a cross domain fusion (CDF) for
reducing the differences between the RGB domain and the frequency domain. We
also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively
improves the performance of the detector, bringing 2.1% and 3.4% improvement in
ExDark and DARK FACE datasets on mAP respectively.Comment: submit to ICRA202
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