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The performance of an object detection system relies heavily on two components: an object model to capture the compositional relationship among the object body and its parts, and a feature representation to describe object appearance. In this work, we present an empirical study of combining two state-of-the-art such components: Deformable Part Model (DPM), a proven effective and flexible part-based...
In this paper, we propose a new local descriptor for action recognition in depth images. The proposed descriptor relies on surface normals in 4D space of depth, time, spatial coordinates and higher-order partial derivatives of depth values along spatial coordinates. In order to classify actions, we follow the traditional Bag-of-words (BoW) approach, and propose two encoding methods termed Multi-Scale...
In this paper, we address the problem of online RGB-D tracking where the target object undergoes significant appearance changes. To sufficiently exploit the color and depth cues, we propose a novel RGB-D tracking framework (DLS) that simultaneously builds the target 2D appearance model and 3D distribution model. The framework decomposes the tracking task into detection, learning and segmentation....
This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused...
Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition. However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of...
Level set-based contour tracking methods have generated recent interest in the computer vision community. In this paper, we propose a novel level set-based algorithm for tracking dynamic implicit contours that utilizes minimal prior information. Our solution consists of two main steps. In the first step, a simple first-order Markov chain model is employed for the coarse localization of a target object...
Target tracking using color based appearance models is very popular in visual tracking. However, trackers based only on color are fragile and often drift to the background when it has similar appearances. In this paper, we propose an efficient way to use distinctive target colors to track the target and eliminate the drift problem. Colors are sampled from the target and its immediate surrounding region...
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