Artificial intelligence has been adopted to facilitate monitoring, operation, and decision in the logistics field. Logistics robots with environment perception capability have been used to improve warehousing efficiency in logistics systems. However, autonomous mobile robots face computationally intensive and real-time demanding tasks such as navigation, localization, and obstacle avoidance. In this article, we present EventTube, an edge computing based event-aware system that can efficiently discover events from the video data captured by RGB-Monoculars and collaborate with individual devices to make timely decisions. EventTube deploys a semantic context extraction pipeline on edge servers to aggregate video streams from mobile robots and feed a few keyframes, including the start and end of the specific events to the successive perception pods, accelerating logistics robots’ response speed. The event-related model parameters are trained and updated online on a server. The video data collected at the warehouse site for our mobile robots show that EventTube significantly improves parcel delivery efficiency without affecting regular deliveries. 

/pdf/eventtube-an-artificial-intelligent-edge-computing-based-2h78uw4e.pdf

EventTube: An Artificial Intelligent Edge Computing Based Event Aware System to Collaborate With Individual Devices in Logistics Systems

Diffusion models have demonstrated remarkable success in generative modeling. In this paper, we propose PADS (Pose Analysis by Diffusion Synthesis), a novel framework designed to address various challenges in 3D human pose analysis through a unified pipeline. Central to PADS are two distinctive strategies: i) learning a task-agnostic pose prior using a diffusion synthesis process to effectively capture the kinematic constraints in human pose data, and ii) unifying multiple pose analysis tasks like estimation, completion, denoising, etc, as instances of inverse problems. The learned pose prior will be treated as a regularization imposing on task-specific constraints, guiding the optimization process through a series of conditional denoising steps. PADS represents the first diffusion-based framework for tackling general 3D human pose analysis within the inverse problem framework. Its performance has been validated on different benchmarks, signaling the adaptability and robustness of this pipeline. 

3D Human Pose Analysis via Diffusion Synthesis

STAFFormer: Spatio-temporal adaptive fusion transformer for efficient 3D human pose estimation

Most existing 3D human pose estimation work rely heavily on the powerful memory capability of networks to obtain suitable 2D-3D mappings from the training data. Few works have studied the modeling of human posture deformation in motion. In this paper, we propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton, and a frame-by-frame skeleton deformation. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence, and then sum them up to obtain the pose of each frame. Subsequently, a loss term based on the diffusion model is used to ensure that the pipeline learns the correct prior motion knowledge. Finally, we have evaluated our proposed method on mainstream datasets and obtained superior results outperforming the state-of-the-art.

pdf/unsupervised-3d-pose-estimation-with-non-rigid-structure-57c0wg8ary.pdf

Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling

Traditional methods for human localization and pose estimation (HPE), which mainly rely on RGB images as an input modality, confront substantial limitations in real-world applications due to privacy concerns. In contrast, radar-based HPE methods emerge as a promising alternative, characterized by distinctive attributes such as through-wall recognition and privacy-preserving, rendering the method more conducive to practical deployments. This paper presents a Radar Tensor-based human pose (RT-Pose) dataset and an open-source benchmarking framework. The RT-Pose dataset comprises 4D radar tensors, LiDAR point clouds, and RGB images, and is collected for a total of 72k frames across 240 sequences with six different complexity-level actions. The 4D radar tensor provides raw spatio-temporal information, differentiating it from other radar point cloud-based datasets. We develop an annotation process using RGB images and LiDAR point clouds to accurately label 3D human skeletons. In addition, we propose HRRadarPose, the first single-stage architecture that extracts the high-resolution representation of 4D radar tensors in 3D space to aid human keypoint estimation. HRRadarPose outperforms previous radar-based HPE work on the RT-Pose benchmark. The overall HRRadarPose performance on the RT-Pose dataset, as reflected in a mean per joint position error (MPJPE) of 9.91cm, indicates the persistent challenges in achieving accurate HPE in complex real-world scenarios. RT-Pose is available at https://huggingface.co/datasets/uwipl/RT-Pose.

RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark

/pdf/monocular-3d-localization-of-vehicles-in-road-scenes-4n92hmyq05.pdf

Monocular 3D Localization of Vehicles in Road Scenes

With the rapid developments of computer vision and deep learning technologies, artificial intelligence takes a more and more important role in sports analyses. In this paper, to attain the objective of automated golf swing analyses, we propose a lightweight temporal-based 2D human pose estimation (HPE) method, called GolfPose, which achieves improved performance than the state-of-the-art image-based HPE methods. Unlike traditional image-based methods, our temporal-based method, designed for efficient and effective golf swing analyses, takes advantage of the temporal information to improve the estimation accuracy of fast-moving and partially self-occluded keypoints. Furthermore, in order to make sure the golf swing analyses can run on mobile devices, we optimize the model architecture to achieve real-time inference. With around 10% of the parameters and half of the GFLOPs used in the state-of-the-art HRNet, our proposed GolfPose model can achieve 9.16 mean pixel error (MPE) in our golf swing dataset, compared with 9.20 MPE for HRNet. Furthermore, the proposed temporal-based method, facilitated with golf club detection(GCD), significantly improves the accuracy of keypoints on the golf club from 13.98 to 9.21 MPE.

GolfPose: Golf Swing Analyses with a Monocular Camera Based Human Pose Estimation

The confidence scores of 2D pose estimation are widely utilized in various fields, including multi-view 3D human pose estimation, skeleton-based human tracking, human action recognition, human re-identification, etc. Despite widespread use, confidence scores from 2D pose estimation methods are unreliable in indicating the accuracy of estimation results, particularly in occlusion situations, i.e., keypoints with high confidence scores may have low accuracy and vice versa. To address this issue, we propose a new 2D human pose estimation calibration method in this paper. Our method not only enhances the accuracy of 2D pose estimation but also aligns the confidence scores with the quality and visibility of keypoints. We achieve 77.6 mAP in the COCO val dataset, compared with 76.5 mAP of the original HRNet. For key-point visibility prediction, we can reach 89.4% accuracy, 87.6% precision, and 97.0% recall in the COCO val dataset. 

2D Human Pose Estimation Calibration and Keypoint Visibility Classification

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