STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

Cong, Peishan; Zhu, Xinge; Feng, Qian; Ren, Yiming; Peng, Xidong; Hou, Yuenan; Xu, Lu; Yang, Ruigang; Manocha, Dinesh; Ma, Yuexin

doi:10.1109/cvpr52688.2022.01899

Cited by 20 publications

(8 citation statements)

References 38 publications

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“…STCrowd. STCrowd (Cong et al 2022) is released recently and the total number of pedestrians is 219 K. There are 5263 and 2988 images with a size of 1280×720 in the training set and validation set, respectively.…”

Section: Experiments Dataset and Evaluation Metricsmentioning

confidence: 99%

GigaHumanDet: Exploring Full-Body Detection on Gigapixel-Level Images

Liu,

Wei,

Yang

et al. 2024

AAAI

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Performing person detection in super-high-resolution images has been a challenging task. For such a task, modern detectors, which usually encode a box using center and width/height, struggle with accuracy due to two factors: 1) Human characteristic: people come in various postures and the center with high freedom is difficult to capture robust visual pattern; 2) Image characteristic: due to vast scale diversity of input (gigapixel-level), distance regression (for width and height) is hard to pinpoint, especially for a person, with substantial scale, who is near the camera. To address these challenges, we propose GigaHumanDet, an innovative solution aimed at further enhancing detection accuracy for gigapixel-level images. GigaHumanDet employs the corner modeling method to avoid the potential issues of a high degree of freedom in center pinpointing. To better distinguish similar-looking persons and enforce instance consistency of corner pairs, an instance-guided learning approach is designed to capture discriminative individual semantics. Further, we devise reliable shape-aware bodyness equipped with a multi-precision strategy as the human corner matching guidance to be appropriately adapted to the single-view large scene. Experimental results on PANDA and STCrowd datasets show the superiority and strong applicability of our design. Notably, our model achieves 82.4% in term of AP, outperforming current state-of-the-arts by more than 10%.

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Section: Experiments Dataset and Evaluation Metricsmentioning

confidence: 99%

GigaHumanDet: Exploring Full-Body Detection on Gigapixel-Level Images

Liu,

Wei,

Yang

et al. 2024

AAAI

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“…Over the past decade, AI technology has made significant advances [111]- [122]. This progress has motivated some researchers to apply AI for vehicle sideslip angle estimation, utilizing a data-driven approach that incorporates input from multi-modal sensors.…”

Section: Aia Approachmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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“…Traffic datasets that are focused on pedestrians have become more abundant [259][260][261][262][263][264][265][266][267][268][269][270], but there exist only datasets with estimated articulated labels for pedestrians [157]. Even though progress has been made on marker-less human motion capture [116] the methods need to be made robust for multiple humans at a distance and in the presence of occlusions.…”

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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STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

Cited by 20 publications

References 38 publications

GigaHumanDet: Exploring Full-Body Detection on Gigapixel-Level Images

GigaHumanDet: Exploring Full-Body Detection on Gigapixel-Level Images

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Semantic Synthesis of Pedestrian Locomotion

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