RELIEF: Joint Low-Light Image Enhancement and Super-Resolution with Transformers

Aakerberg, Andreas; Nasrollahi, Kamal; Moeslund, Thomas B.

doi:10.1007/978-3-031-31435-3_11

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Human and object detection in traffic from alternative sensors such as Radar [201], LiDAR [143,[202][203][204][205][206], event cameras [207] have been studied as a way to boost object and human detection performance. Methods to improve lowquality image data by reducing motion blur [208], increasing image quality in low light or low-resolution images [209] performing detection on RAW images [210], or object in-painting to recover from occlusions [211] could possibly greatly improve human sensing in real traffic data.…”

Section: Developments In the Fieldmentioning

confidence: 99%

Semantic Synthesis of Pedestrian Locomotion

Priisalu

Păduraru

Pirinen

et al. 2021

Computer Vision – ACCV 2020

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It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by motion blur and occlusions occur often. This has led to the popularisation of traffic data-specific benchmarks. Recently Light Detection And Ranging (LiDAR) sensors have become popular to directly estimate depths without the need to perform 3D reconstructions. However, LiDAR-based methods still lack in articulated human detection at a distance when compared to image-based methods. We hypothesize that benchmarks targeted at articulated human sensing from LiDAR data could bring about increased research in human sensing and prediction in traffic and could lead to improved traffic safety for pedestrians.

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