Articulated part-based model for joint object detection and pose estimation

By Min Sun, Silvio Savarese. 2011

213 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Towards Accurate Multi-person Pose Estimation in the Wild (2017, methodology): “D person detection is used as a ﬁrst step in several 3D pose estimation works [39, 4, 5].”
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields (2016, methodology): “A common approach [28, 11, 33, 16] is to divide and conquer the problem by employing a person detector and performing single-person pose estimation for each detection.”
Convolutional Pose Machines (2016, background): “Hierarchical models [35, 37] represent the relationships between parts at different scales and sizes in a hierarchical tree structure.”
Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts (2014, methodology): “Similar to our method, [1, 23, 3] use additional annotations to provide supervision for a part-based model, while Poselets [2] and [14] use keypoints and masks annotations to cluster objects or their parts.”
Articulated Human Detection with Flexible Mixtures of Parts (2013, background): “Large-scale parts can be integrated into a hierarchical, coarse-to-fine representation [37], [38].”
Pose Machines: Articulated Pose Estimation via Inference Machines (2014, methodology): “Recent methods have also explored using part hierarchies [16,17] and condition the detection of smaller parts that model regions around anatomical joints on the localization of larger composite parts or poselets [11,10,18,19] that model limbs in canonical configurations and tend to be easier to dete”
Do Semantic Parts Emerge in Convolutional Neural Networks? (2016, background): “The most notable examples of works on semantic part models are fine-grained recognition (Lin et al, 2015; Zhang et al, 2014; Parkhi et al, 2012), object class detection (Chen et al, 2014), articulated pose estimation (Liu et al, 2014; Sun and Savarese, 2011; Ukita, 2012), and attribute prediction (Z”
Multi-person Pose Estimation with Local Joint-to-Person Associations (2016, methodology): “[22] and [24] perform non-maximum suppression on the marginals obtained using a graphical model to generate multiple pose hypotheses in an image.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Traditionally, a two-staged approach was used by first detecting body parts and then estimating the pose as in (Pishchulin et al. (2012); Gkioxari et al. (2014); Sun & Savarese (2011)).”
An Efficient Convolutional Network for Human Pose Estimation (2016, background): “Another line of research is based on Hierarchical models [24, 26] that first detect larger parts in the image and then condition the detection of difficult smaller parts on detected larger parts.”
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images (2014, background): “This has been addressed by proposing richer structures that aim to resolve the weakness of a tree model by adding additional constraints between the limbs [10], [11], [12], [13] or using a fully connected graphical model [14], [15].”
Do Convnets Learn Correspondence? (2014, methodology): “Human pose estimation has been approached using tree structured methods to model the spatial relationships between parts [12, 13, 14], and also using poselets [15] as an intermediate step to localize human keypoints [16, 17].”
Multi-source Deep Learning for Human Pose Estimation (2014, background): “On the other hand, many recent works use local body parts [49, 51, 10, 55, 8, 12, 46, 39, 1, 40, 20, 44, 52] in order to handle the many degrees of freedom in body part articulation.”
Detecting Actions, Poses, and Objects with Relational Phraselets (2012, methodology): “Our model jointly addresses detection, action classification, and pose estimation, similar to part-models that jointly reason about actions and pose [8], and detection and pose [9, 11].”
Object Detection Using Strongly-Supervised Deformable Part Models (2012, unknown): “The task of part localization addressed in [10, 16, 24], however, is different from the task of object detection in this paper.”
Joint Object and Part Segmentation Using Deep Learned Potentials (2015, methodology): “Later models use more accurate part-representation through explicit part supervision [34, 46].”
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (2018, methodology): “Multi-Person Pose Estimation For multi-person pose estimation, most approaches [5], [6], [38], [39], [40], [41], [42], [43], [44] have used a top-down strategy that first detects people and then have estimated the pose of each person independently on each detected region.”