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The proof of human parts has an imperative effect on pose evaluation, and can be effortlessly confused with difficult background due to indefinite part detector. This paper circumvents this predicament by performing a proof supporting approach, where each part also receives confidence from its neighborhood which uses the outline information between connect parts and mitigates the risk of being blindly...
We propose a new method for human pose estimation from a single image. Since both appearance and locations of different body parts strongly depends on each other in an image, considering their relationship helps identifying the underlying poses. However, most of the existing methods cannot fully utilize this contextual information by using simplified model to make inference tractable. The proposed...
For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection. Many approaches to pose estimation rely on detecting or tracking parts or keypoints [11, 21]. In this paper we build on a recent state-of-the-art convolutional network for slidingwindow detection [10] to provide detection and rough pose estimation in a single shot, without intermediate stages...
A system that can perform multitasking including face Detection, pose Estimation and landmark Localisation (named DEL) is a core in many face recognition-based applications. Additionally, a wide range of applications that usually require machines to understand human face expressions can be obtained such us autonomous vehicles, patient observation or human-computer interaction (HCI). Typically, a DEL...
We address the problem of articulated 2-D human pose estimation in unconstrained natural images. In previous work the Pictorial Structure Model approach has proven particularly successful, and is appealing because of its moderate computational cost. However, the accuracy of resulting pose estimates has been limited by the use of simple representations of limb appearance. We propose strong discriminatively...
In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20 Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally...
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