In this paper we study the problem of estimating innercyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of poselets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.
In the context of key-frame selection in videos, poselets have been used for human activity recognition. @cite_20 proposed a framework based on poselet activations for selecting key-frames that represent key-states in an action sequence. An additional classifier trained on pairwise terms for all activations then decides if a specific action sequence occurred. @cite_7 select action specific postures by matching shape information from individual frames in order to recognize specific tennis strokes in game footage.
@cite_7 @cite_20
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In this paper, we present a new approach for human action recognition based on key-pose selection and representation. Poses in video frames are described by the proposed extensive pyramidal features (EPFs), which include the Gabor, Gaussian, and wavelet pyramids. These features are able to encode the orientation, intensity, and contour information and therefore provide an informative representation of human poses. Due to the fact that not all poses in a sequence are discriminative and representative, we further utilize the AdaBoost algorithm to learn a subset of discriminative poses. Given the boosted poses for each video sequence, a new classifier named weighted local naive Bayes nearest neighbor is proposed for the final action classification, which is demonstrated to be more accurate and robust than other classifiers, e.g., support vector machine (SVM) and naive Bayes nearest neighbor. The proposed method is systematically evaluated on the KTH data set, the Weizmann data set, the multiview IXMAS data set, and the challenging HMDB51 data set. Experimental results manifest that our method outperforms the state-of-the-art techniques in terms of recognition rate. Action detection is of great importance in understanding human motion from video. Compared with action recognition, it not only recognizes action type, but also localizes its spatiotemporal extent. This paper presents a relational model for action detection, which first decomposes human action into temporal “key poses” and then further into spatial “action parts”. Specifically, we start by clustering cuboids around each human joint into dynamic-poselets using a new descriptor. The cuboids from the same cluster share consistent geometric and dynamic structure, and each cluster acts as a mixture of body parts. We then propose a sequential skeleton model to capture the relations among dynamic-poselets. This model unifies the tasks of learning the composites of mixture dynamic-poselets, the spatiotemporal structures of action parts, and the local model for each action part in a single framework. Our model not only allows to localize the action in a video stream, but also enables a detailed pose estimation of an actor. We formulate the model learning problem in a structured SVM framework and speed up model inference by dynamic programming. We conduct experiments on three challenging action detection datasets: the MSR-II dataset, the UCF Sports dataset, and the JHMDB dataset. The results show that our method achieves superior performance to the state-of-the-art methods on these datasets. Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be tightly clustered both in the configuration space of keypoints, as well as in the appearance space of image patches. In this paper we develop a new algorithm for detecting people using poselets. 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 of each can provide vital disambiguating information, just as object detection can be improved by considering the detection scores of nearby objects in the scene. This can be done by training a two-layer feed-forward network with weights set using a max margin technique. The refined poselet activations are then clustered into mutually consistent hypotheses where consistency is based on empirically determined spatial keypoint distributions. Finally, bounding boxes are predicted for each person hypothesis and shape masks are aligned to edges in the image to provide a segmentation. To the best of our knowledge, the resulting system is the current best performer on the task of people detection and segmentation with an average precision of 47.8 and 40.5 respectively on PASCAL VOC 2009. Human action is, in general, characterized by a sequence of specific body postures. An action is often recognizable from a single view of an action specific posture. In this paper we demonstrate that specific actions can be recognized in long video sequence by matching shape information extracted from individual frames to stored prototypes representing key frames of the action. The matching algorithm is tolerant to substantial deformation between image and prototype and recognizes qualitatively similar image shapes produced by the body postures. The algorithm is applied to the recognition of specific tennis strokes in up to 2 min long video sequences. Using key-frames within the sequence all (1015) forehand strokes are found with no false positives. The backhand strokes are detected with somewhat lower performance. We conclude that this type of approach to action recognition has large potential for application to automatic video analysis and editing.