Recognizing human actions from still images with latent poses

By Weilong Yang, Yang Wang, Greg Mori. 2010

233 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning human activities and object affordances from RGB-D videos (2012, background): “There has been a lot of work on human activity detection from images (Yang et al., 2010; Yao et al., 2011) and from videos (Laptev et al.”
Articulated Human Detection with Flexible Mixtures of Parts (2013, background): “Such models were originally developed for human detection, but [36] extends them to pose estimation.”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, background): “Some works use human pose for action recognition by detecting local pose features [25] and modeling the spatial configuration between human body parts and objects [5], [26], [27], [28], [29].”
Joint action recognition and pose estimation from video (2015, background): “For certain actions defined by specific geometric configuration of body parts, pose estimation from a single image may be sufficient for action recognition [15, 28, 5, 32].”
Measuring and reducing observational latency when recognizing actions (2011, methodology): “Defining and measuring the observational latency of a system involves subtle decisions.”
Exploring discriminative pose sub-patterns for effective action classification (2013, background): “Another recent research trend to employ pose as a cue in action recognition is closely related to the concept of “poselets” [22, 12, 32].”
Learning person-object interactions for action recognition in still images (2011, background): “[38] model the body pose as a latent variable for action recognition.”
HICO: A Benchmark for Recognizing Human-Object Interactions in Images (2015, background): “detecting human body parts) by deriving feature representations based on human poses [11, 38, 21, 30, 32, 31], whereas others focus more on spatial relations between humans and objects [34, 33, 23, 9, 5, 4].”
Learning Feature Pyramids for Human Pose Estimation (2017, background): “, activity recognition [60, 54], clothing parsing [57, 58, 36], human re-identification [65], and human-computer interaction.”
View-Invariant Action Recognition Using Latent Kernelized Structural SVM (2012, background): “object recognition with attributes [17], image annotation and segmentation [18], action recognition from pose estimation [19], and group activity recognition [20].”
Action Recognition with Exemplar Based 2.5D Graph Matching (2012, background): “The idea of using human poses for action recognition has been studied in some previous work which either detect local pose features [4, 6] or model the spatial configuration between human body parts and objects [2, 3, 7, 8].”
Coupled Action Recognition and Pose Estimation from Multiple Views (2012, background): “2005), previous works (Thurau and Hlavac 2008; Kläser et al. 2010; Natarajan et al. 2010; Yang et al. 2010) have encoded pose implicitly.”
A survey on still image based human action recognition (2014, methodology): “When performing different actions, e.g., throwing a ball or using a computer, the body parts, e.g., the arms, can be in different locations or with different poses.”
Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses (2012, unknown): “Recognizing human activities in still images is a new problem, yet it has received much attention in recent years [14], [15], [42], [43], [44], [45], [46].”
Multiple/Single-View Human Action Recognition via Part-Induced Multitask Structural Learning (2015, methodology): “Motivated by the recent work on attribute-based classification [18] and the part-based classification [34], [40], [41], [44], it would”
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization (2015, background): “…a wide range of computer vision tasks including object detection (Felzenszwalb et al., 2010; Chen et al., 2014), pose estimation (Yang & Ramanan, 2011; Chen & Yuille, 2014), semantic segmentation (Long et al., 2015; Chen et al., 2015) and action recognition (Yang et al., 2010; Zhu et al., 2013).”
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation (2013, background): “There has been a considerable amount of previous work on detection of human activities from still images as well as videos (e.g., Maji et al., 2011; Yang et al., 2010; Xing et al., 2008; Ryoo, 2011; Hoai & De la Torre, 2012a).”
Understanding egocentric activities (2011, background): “[34] treat the pose of the person in an image as a latent variable and use it to enhance action recognition.”