multi-person
Papers with tag multi-person
2022
- JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and TrackingEdward Vendrow, Duy Tho Le, and Hamid RezatofighiIn 2022
Autonomous robotic systems operating in human environments must understandtheir surroundings to make accurate and safe decisions. In crowded human sceneswith close-up human-robot interaction and robot navigation, a deepunderstanding requires reasoning about human motion and body dynamics over timewith human body pose estimation and tracking. However, existing datasets eitherdo not provide pose annotations or include scene types unrelated to roboticapplications. Many datasets also lack the diversity of poses and occlusionsfound in crowded human scenes. To address this limitation we introduceJRDB-Pose, a large-scale dataset and benchmark for multi-person pose estimationand tracking using videos captured from a social navigation robot. The datasetcontains challenge scenes with crowded indoor and outdoor locations and adiverse range of scales and occlusion types. JRDB-Pose provides human poseannotations with per-keypoint occlusion labels and track IDs consistent acrossthe scene. A public evaluation server is made available for fair evaluation ona held-out test set. JRDB-Pose is available at https://jrdb.erc.monash.edu/ .
使用的是全景相机，而不是普通多视角相机
dataset multi-person pose-estimation tracking@inproceedings{JRDB-Pose, title = {JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Tracking}, author = {Vendrow, Edward and Le, Duy Tho and Rezatofighi, Hamid}, year = {2022}, tags = {dataset, multi-person, pose-estimation, tracking}, } - AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose RegressionYabo Xiao, Xiaojuan Wang, Dongdong Yu, Kai Su, Lei Jin, Mei Song, Shuicheng Yan, and Jian ZhaoIn 2022
Multi-person pose estimation generally follows top-down and bottom-upparadigms. Both of them use an extra stage (\boldsymbole.g., humandetection in top-down paradigm or grouping process in bottom-up paradigm) tobuild the relationship between the human instance and corresponding keypoints,thus leading to the high computation cost and redundant two-stage pipeline. Toaddress the above issue, we propose to represent the human parts as adaptivepoints and introduce a fine-grained body representation method. The novel bodyrepresentation is able to sufficiently encode the diverse pose information andeffectively model the relationship between the human instance and correspondingkeypoints in a single-forward pass. With the proposed body representation, wefurther deliver a compact single-stage multi-person pose regression network,termed as AdaptivePose. During inference, our proposed network only needs asingle-step decode operation to form the multi-person pose without complexpost-processes and refinements. We employ AdaptivePose for both 2D/3Dmulti-person pose estimation tasks to verify the effectiveness of AdaptivePose.Without any bells and whistles, we achieve the most competitive performance onMS COCO and CrowdPose in terms of accuracy and speed. Furthermore, theoutstanding performance on MuCo-3DHP and MuPoTS-3D further demonstrates theeffectiveness and generalizability on 3D scenes. Code is available athttps://github.com/buptxyb666/AdaptivePose.
multi-person monocular pose-estimation@inproceedings{AdaptivePose++, title = {AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression}, author = {Xiao, Yabo and Wang, Xiaojuan and Yu, Dongdong and Su, Kai and Jin, Lei and Song, Mei and Yan, Shuicheng and Zhao, Jian}, year = {2022}, tags = {multi-person, monocular, pose-estimation}, }