We describe a method for retrieving shots containing a particular 2D human pose from unconstrained movie and TV videos. The method involves first localizing the spatial layout of the head, torso and limbs in individual frames using pictorial structures, and associating these through a shot by tracking. A feature vector describing the pose is then constructed from the pictorial structure. Shots can be retrieved either by querying on a single frame with the desired pose, or through a pose classifier trained from a set of pose examples. Our main contribution is an effective system for retrieving people based on their pose, and in particular we propose and investigate several pose descriptors which are person, clothing, background and lighting independent. As a second contribution, we improve the performance over existing methods for localizing upper body layout on unconstrained video. We compare the spatial layout pose retrieval to a baseline method where poses are retrieved using a HOG descriptor. Performance is assessed on five episodes of the TV series 'Buffy the Vampire Slayer', and pose retrieval is demonstrated also on three Hollywood movies.
The effect of iron on the uniformity of the field produced by an axisymmetric thick solenoid is considered. Using a Fourier series approach, an exact solution for the vector potential A is found and from this the magnetic induction B is derived. The results are compared with those obtained from previous approximate methods.
The objectives of the work described in this paper are simply stated: given examples of a particular person and an unlabelled video, we wish to find every instance of that person in the video and in others. This is an extremely difficult problem because of the many sources of variation in the person's appearance. We present a two stage approach. A 3-D ellipsoid approximation of the person's head is used to train a set of generative parts-based 'constellation' models which propose candidate detections in an image. The detected parts are then used to align the model, and the detections verified by global appearance. Novel aspects of the approach include the minimal supervision required and the generalization across a wide range of pose. We demonstrate results of detecting three characters in a TV situation comedy.
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