. Unlike that work which used 3D annotations of keypoints, we use only 2D annotations which are much easier for naive human annotators. The main algorithmic contribution is in how we use the pattern of poselet activations. Individual poselet activations are noisy, but considering the spatial context

In this paper, we propose techniques to make use of two complementary bottom-up features, image edges and texture patches, to guide top-down object segmentation towards higher precision. We build upon the part-based poselet detector, which can predict masks for numerous parts of an object

In this paper we consider the challenging problem of ar-ticulated human pose estimation in still images. We observe that despite high variability of the body articulations, hu-man motions and activities often simultaneously constrain the positions of multiple body parts. Modelling such higher order

activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded environments – the scenario most difficult to analyse because of occlusions. In particular, we extend the Poselet detector approach by defining a descriptor based on Poselet activation patterns over time

to identify Poselet activations. By combining keypoint predictions from poselet activitions within a graphical model, we can infer the marginal distribution over each keypoint without any kinematic constraints. Our approach is demonstrated on a new publicly available dataset with promising results. 1.

-fined for the foreground regions, while combined foreground-background descriptors still capture the context of an action. Also a low-dimensional global camera motion descriptor can be computed. Poselet activations in the foreground area indicate the actor and its pose. We propose track-ing these poselets to obtain

max-margin discriminative framework, where we treat keyframes as latent variables. This allows us to (jointly) learn a set of most discriminative keyframes while also learning the local temporal context between them. Keyframes are encoded using a spatially-localizable poselet-like representation

Human activity is a persistent subject of interest in the last decade. On the one hand, video sequences provide a huge volume of motion information in order to recognize the human active actions. On the other hand, the spatial information about static human poses is valuable for human action

We present a distributed representation of pose and appearance of people called the “poselet activation vector”. First we show that this representation can be used to estimate the pose of people defined by the 3D orientations of the head and torso in the challenging PASCAL VOC 2010 person detection

We propose the use of a robust pose feature based on part based human detectors (Poselets) for the task of ac-tion recognition in relatively unconstrained videos, i.e., col-lected from the web. This feature, based on the original poselets activation vector, coarsely models pose and its transitions