jmlr jmlr2012 jmlr2012-32 knowledge-graph by maker-knowledge-mining

32 jmlr-2012-Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition

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Author: Yang Wang, Duan Tran, Zicheng Liao, David Forsyth

Abstract: We consider the problem of parsing human poses and recognizing their actions in static images with part-based models. Most previous work in part-based models only considers rigid parts (e.g., torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate. In this paper, we introduce hierarchical poselets—a new representation for modeling the pose conﬁguration of human bodies. Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e.g., torso + left arm). In the extreme case, they can be the whole bodies. The hierarchical poselets are organized in a hierarchical way via a structured model. Human parsing can be achieved by inferring the optimal labeling of this hierarchical model. The pose information captured by this hierarchical model can also be used as a intermediate representation for other high-level tasks. We demonstrate it in action recognition from static images. Keywords: human parsing, action recognition, part-based models, hierarchical poselets, maxmargin structured learning

Reference: text

Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 EDU Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, USA Editor: Isabelle Guyon and Vassilis Athitsos Abstract We consider the problem of parsing human poses and recognizing their actions in static images with part-based models. [sent-6, score-0.732]

2 In this paper, we introduce hierarchical poselets—a new representation for modeling the pose conﬁguration of human bodies. [sent-11, score-0.59]

3 Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e. [sent-12, score-1.074]

4 The hierarchical poselets are organized in a hierarchical way via a structured model. [sent-16, score-0.633]

5 Keywords: human parsing, action recognition, part-based models, hierarchical poselets, maxmargin structured learning 1. [sent-20, score-0.541]

6 , faces and cars) which can be reasonably modeled using several prototypical templates, human bodies are much more difﬁcult to model due to the wide variety of possible pose conﬁgurations. [sent-24, score-0.551]

7 A part-based model represents the human body as a constellation of a set of rigid parts (e. [sent-28, score-0.723]

8 Part-based models have been used extensively in various computer vision applications involving humans, such as human parsing (Felzenszwalb and Huttenlocher, 2005; Ramanan, 2006), kinematic tracking (Ramanan et al. [sent-34, score-0.637]

9 The ﬁrst problem is human parsing, also known as human pose estimation. [sent-69, score-0.767]

10 We infer the human pose using this hierarchical representation. [sent-79, score-0.59]

11 The hierarchical poselet also provides rich information about body poses that can be used in other applications. [sent-80, score-0.568]

12 To demonstrate this, we apply it to recognize human action in static images. [sent-81, score-0.532]

13 In this application, we use hierarchical poselets to capture various pose information of the human 3076 D ISCRIMINATIVE H IERARCHICAL PART- BASED M ODELS body, this information is further used as some intermediate representation to infer the action of the person. [sent-82, score-1.251]

14 Section 2 reviews previous work in human parsing and action recognition. [sent-86, score-0.618]

15 Section 4 describes how to use hierarchical poselets for human parsing. [sent-88, score-0.81]

16 Section 5 develops variants of hierarchical poselets for recognizing human action in static images. [sent-89, score-1.107]

17 We present experimental results on human parsing and action recognition in Section 6 and conclude in Section 7. [sent-90, score-0.688]

18 In this section, we brieﬂy review previous work in human parsing and action recognition that is most related to our work. [sent-93, score-0.688]

19 Human action recognition: Most of the previous work on human action recognition focuses on videos. [sent-122, score-0.723]

20 Compared with videos, human action recognition from static images is a relatively less-studied area. [sent-131, score-0.704]

21 In a nutshell, poselets refer to pieces of human poses that are tightly clustered in both appearance and conﬁguration spaces. [sent-146, score-0.889]

22 Hierarchical poselets extend the original poselets in several important directions to make them more appropriate for human parsing. [sent-150, score-1.191]

23 Beyond rigid “parts”: Most of the previous work in part-based human modeling are based on the notion that the human body can be modeled as a set of rigid parts connected in some way. [sent-152, score-1.171]

24 This phenomenon was observed even prior to the work of poselet and was exploited to detect stylized human poses and build appearance models for kinematic tracking (Ramanan et al. [sent-160, score-0.795]

25 Multiscale hierarchy of “parts”: Another important property of our representation is that we deﬁne “parts” at different levels of hierarchy to cover pieces of human poses at various granularity, ranging from the conﬁguration of the whole body, to small rigid parts. [sent-162, score-0.564]

26 In particular, we deﬁne 20 parts to represent the human pose and organize them in a hierarchy shown in Figure 1. [sent-163, score-0.622]

27 To avoid terminological confusion, we will use “part” to denote one of the 20 parts in Figure 1 and use “primitive part” to denote rigid body parts (i. [sent-164, score-0.578]

28 from the images and form a set of poselets for that part. [sent-181, score-0.567]

29 For example, we use cells of 12 × 12 pixel regions for poselets of the whole body, and cells of 2 × 2 for poselets of the upper/lower arm. [sent-187, score-0.961]

30 Examples of poselets and their corresponding HOG templates for other body parts are shown in Figure 3. [sent-196, score-0.77]

31 This information will be used in human parsing when we need to infer the endpoints of a primitive part for a test image. [sent-200, score-0.533]

32 Human Parsing In this section, we describe how to use hierarchical poselets in human parsing. [sent-202, score-0.81]

33 We ﬁrst develop an undirected graphical model to represent the conﬁguration of the human pose (Section 4. [sent-203, score-0.506]

34 1 Model Formulation We denote the complete conﬁguration of a human pose as L = {li }K , where K is the total number i=1 of parts (i. [sent-209, score-0.622]

35 Here αi; j;zi ;z j is a model parameter that favors certain relative spatial bins when poselets zi and z j are chosen for parts i and j, respectively. [sent-233, score-0.672]

36 Local appearance φ(li ; I): This potential function captures the compatibility of placing the poselet zi at the location (xi , yi ) of an image I. [sent-235, score-0.55]

37 In other words, the score of placing the poselet zi at image location (xi , yi ) is a linear combination (with bias term) of the responses all the poselet templates at (xi , yi ) for part i. [sent-243, score-0.767]

38 We use poselets to capture distinctive appearance 3081 WANG , T RAN , L IAO AND F ORSYTH patterns of various parts. [sent-252, score-0.543]

39 These poselets have better discriminative powers than traditional rigid part detectors. [sent-253, score-0.678]

40 For example, look at the examples in Figure 2 and Figure 3, the poselets capture various characteristic patterns for large parts, such as the “A”-shape for the legs in the ﬁrst row of Figure 2. [sent-254, score-0.555]

41 Another work that uses hierarchical models for human parsing is the AND-OR graph in Zhu et al. [sent-274, score-0.506]

42 Our work on human parsing can be seen as bridging the gap between two popular schools of approaches for human parsing: part-based methods, and exemplar-based methods. [sent-286, score-0.683]

43 Part-based methods, as explained above, model the human body as a collection of rigid parts. [sent-287, score-0.607]

44 If we temporarily ignore the poselet indices zi and z j and think of li = (xi , yi ), we can represent the messages as 2D images and pass messages using techniques similar to those in Ramanan (2006). [sent-304, score-0.51]

45 The inference gives us the image locations and poselet indices of all the 20 parts (both primitive and non-primitive). [sent-311, score-0.555]

46 For each part (please refer to Figure 1), we show the inferred poselet by visualizing two sample patches from the corresponding poselet cluster and the SVM HOG template. [sent-326, score-0.636]

47 If the hypothesized poselet zi is the same as the ground-truth poselet zn for the i-th part, the ﬁrst term of Equation 5 will be zero. [sent-335, score-0.606]

48 The ﬁnal human parsing results are still obtained from conﬁgurations li of primitive parts. [sent-343, score-0.585]

49 Each image is annotated with the action category and joints on the human body. [sent-350, score-0.607]

50 In this section, we demonstrate it in human action recognition from static images. [sent-365, score-0.602]

51 So far most work in human action recognition has been focusing on recognition from videos. [sent-368, score-0.597]

52 , motion) for action recognition, the examples in Figure 5 clearly show that the information conveyed by static images is also an important component of action recognition. [sent-371, score-0.569]

53 In particular, we are interested in exploiting the human pose as a source of information for action recognition. [sent-373, score-0.702]

54 Another approach to static image action recognition is to explicitly recover the human pose, then use the pose as a feature representation for action recognition. [sent-381, score-1.121]

55 It uses a representation based on human pose for action recognition. [sent-390, score-0.702]

56 But instead of explicitly recovering the precise pose conﬁguration, it represents the human pose as a set of latent variables in the model. [sent-391, score-0.751]

57 In this section, we use hierarchical poselets to capture richer pose information for action recognition. [sent-409, score-0.99]

58 In contrast, our pose representation captures a much wider range of information across various pieces of the human body. [sent-419, score-0.542]

59 These poselets cover various portions of the human bodies, including the whole body (1st row), both legs (2nd row), one arm (3nd row), respectively. [sent-425, score-1.081]

60 The training images are labeled with ground-truth action categories and joints on the human body (Figure 5). [sent-426, score-0.792]

61 Large cell sizes are used for poselets of large body parts (e. [sent-442, score-0.74]

62 2 Our Model Let I be an image containing a person, Y ∈ Y be its action label where Y is the action label alphabet, L be the pose conﬁguration of the person. [sent-449, score-0.715]

63 The complete pose conﬁguration is denoted as L = {li }K i=1 (K = 20 in our case), where li = (xi , yi , zi ) represents the 2D image location and the index of the corresponding poselet cluster for the i-th part. [sent-450, score-0.758]

64 Similar to Figure 6, these poselets cover various portions of the human bodies the spatial constraint between the i-th and the j-th parts. [sent-454, score-0.808]

65 Part appearance φY (I, li ): This potential function models the compatibility of the conﬁguration li of the i-th part and the local image patch deﬁned by li = (xi , yi , zi ), under the assumption that the action label is Y . [sent-464, score-0.703]

66 Since our goal is action recognition, we also enforce that the poselet zi should comes from the action Y . [sent-465, score-0.722]

67 In other words, if we deﬁne ZiY as the set of poselet indices for the i-th part corresponding to the action category Y , this potential function is parametrized as: φY (I, li ) = β⊤ · f (I, li ) i,Y −∞ 3088 if zi ∈ ZiY ; otherwise. [sent-466, score-0.734]

68 Again, we enforce poselets zi and z j to come from action Y as follows: ψY (li , l j ) = γ⊤ · bin(li − l j ) i,Y −∞ if zi ∈ ZiY , z j ∈ ZY ; j otherwise. [sent-471, score-0.769]

69 Note that if the potential functions and model parameters in Equations(7,8,9,10) do not depend on the action label Y , the part appearance φ(·) and pairwise part constraint ψ(·) exactly recover the human parsing model in Section 4. [sent-473, score-0.748]

70 ′ L 3089 WANG , T RAN , L IAO AND F ORSYTH head + upper arm Buffy head + lower arm UIUC people sport images Buffy UIUC people sport images Figure 8: Scatter plots of heads (red) and upper/lower arms (blue and green) with respect to ﬁxed upper body position on three data sets. [sent-491, score-0.996]

71 3090 D ISCRIMINATIVE H IERARCHICAL PART- BASED M ODELS Ours PS IIP Ours PS IIP Ours PS IIP Figure 9: Examples of human body parsing on the UIUC people data set. [sent-523, score-0.672]

72 We also show the result (3rd row, Table 1(a)) of using only the basic-level poselets corresponding to the rigid parts. [sent-541, score-0.652]

73 It is clear that our full model using hierarchical poselets outperforms using rigid parts alone. [sent-542, score-0.852]

74 So our model can be seen as a principled way of unifying human pose estimation, person detection, and many other areas related to understanding humans. [sent-549, score-0.577]

75 In the ﬁrst row of Table 2, we show the results of person detection on the UIUC people data set by running our human parsing model, then picking the bounding box corresponding to the part “whole body” as the detection. [sent-550, score-0.646]

76 In our method, the conﬁguration of the poselets corresponding to the whole body can be directly used for person detection. [sent-653, score-0.726]

77 3092 D ISCRIMINATIVE H IERARCHICAL PART- BASED M ODELS Ours PS IIP Ours PS IIP Ours PS IIP Figure 11: Examples of human body parsing on the sport image data set. [sent-654, score-0.736]

78 3 K INEMATIC T RACKING To further illustrate our method, we apply the model learned from the UIUC people data set for kinematic tracking by independently parsing the human ﬁgure in each frame. [sent-674, score-0.644]

79 Even in situations where the small primitive parts are hard to detect, our method can still reason about the plausible pose conﬁguration by pulling information from large pieces of the human bodies. [sent-695, score-0.743]

80 Both data sets contain images of people with ground-truth pose annotations and action labels. [sent-699, score-0.634]

81 (2010) have annotated the pose with 14 joints on the human body on all the images in the data set. [sent-707, score-0.813]

82 dancing playing golf sitting running walking Figure 13: Visualization of some inferred poselets on the still image data set. [sent-726, score-0.745]

83 In Figure 13, we visualize several inferred poselets on some examples whose action categories are correctly classiﬁed. [sent-743, score-0.689]

84 Each poselet is visualized by showing several patches from the corresponding poselet cluster. [sent-744, score-0.61]

85 3095 WANG , T RAN , L IAO AND F ORSYTH athletics badminton baseball soccer tennis volleyball Figure 14: Visualization of some inferred poselets on the Leeds sport data set. [sent-745, score-0.598]

86 But if we examine the poselets carefully, we can see that various pieces of the football player are very similar to those found in the dancing action. [sent-777, score-0.65]

87 The action categories (American football, croquet and ﬁeld hockey) for the examples in Figure 15 are disjoint from the action categories of the still image data set. [sent-786, score-0.571]

88 More importantly, our model outputs poselets for various parts which support its prediction. [sent-790, score-0.581]

89 For example, we can say it is closer to “dancing” than “playing golf” because the pose of the football player in the image is similar to certain type of dancing legs, and certain type of dancing arms. [sent-792, score-0.554]

90 Different poselets in our representation capture human poses at various levels of granularity. [sent-795, score-0.775]

91 Some poselets correspond to the rigid parts typically used in previous work. [sent-796, score-0.768]

92 The advantage of this representation is that it infers the human pose by pulling information across various levels of details, ranging from the coarse shape of the whole body, to the ﬁne-detailed information of small rigid parts. [sent-799, score-0.724]

93 We have demonstrate the applications of this rep3097 WANG , T RAN , L IAO AND F ORSYTH resentation in human parsing and human action recognition from static images. [sent-800, score-1.024]

94 This will be important in order to extend hierarchical poselets to other objects (e. [sent-803, score-0.549]

95 Poselets: Body part detectors training using 3d human pose annotations. [sent-814, score-0.559]

96 Combining discriminative appearance and segmentation cues for articulated human pose estimation. [sent-879, score-0.637]

97 Clustered pose and nonlinear appearance models for human pose estimation. [sent-882, score-0.829]

98 A hierarchical model of shape and appearance for human action classiﬁcation. [sent-929, score-0.619]

99 Efﬁcient inference with multiple heterogenous part detectors for human pose estimation. [sent-961, score-0.559]

100 Multiple tree models for occlusion and spatial constraints in human pose estimation. [sent-985, score-0.543]

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