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The problem of person re-identification, identifying the same person appeared in different camera views, is an important and challenging task in computer vision that has high potential application in areas like visual surveillance. In this paper we introduce a new feature fusion strategy for person reidentification that combines low-level Weighted Histograms of Overlapping Stripes (WHOS) features...
Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth-size relationship estimation take full advantage...
Human face is a very important research subject in computer vision due to its wide application prospect. However, pose, illumination and expression (PIE) variations challenge the robustness offace descriptions. Due to the unique structure and human perception of faces, facial parts are always considered most representative and discriminative in the whole face. In this paper, we propose a novel face...
Clothing attributes, of which color plays an important role, are receiving more and more interests in machine vision researches and applications because of their uses and effectiveness in tasks like pedestrian analysis. However, color description is a challenging problem due to complex environments such as illumination variations. Most prior works describe color attributes using only low-level features...
Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible...
Person re-identification is an important problem in visual surveillance where appearance plays a key role. Color is one of the widely used appearance features and utilizing more color spaces doesn't imply benefit of performance enhancement. That's because the poor performance color spaces influence on the high ones. So it is significant to evaluate the performance of different color spaces for person...
Hair is an important aspect of human appearance. Hair color has been employed to facilitate face retrieval in literature, but hair style is still dismissed because of the challenges of its segmentation. In this paper, we propose a novel hair style retrieval algorithm in unconstrained environments. In contrary to defining similarity based on features, we base our measurement directly on hair shapes...
Video Object Segmentation (VOS) is to cut out a selected object from video sequences, where the main difficulties are shape deformation, appearance variations and background clutter. To cope with these difficulties, we propose a novel method, named as Hierarchical Localized Classification of Regions (HLCR). We suggest that appearance models as well as the spatial and temporal coherence between frames...
In this paper, we propose a general video object segmentation framework which views object segmentation from a unified Bayesian perspective and optimizes the MAP formulated problem in a progressive manner. Based on object detection and tracking results, a three-level hierarchical video object segmentation approach is presented. At the first level, an offline learned segmentor is applied to each object...
Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both...
This paper focuses on the problem of tracking multiple humans in dense environments which is very challenging due to recurring occlusions between different humans. To cope with the difficulties it presents, an offline boosted multi-view upper-body detector is used to automatically initialize a new human trajectory and is capable of dealing with partial human occlusions. What is more, an online learning...
This paper proposes a novel approach to boost a set of Associated Pairing Comparison Features (APCFs) in Granular Space for pedestrian detection, in which Pairing Comparison of Color (PCC) and Pairing Comparison of Gradient (PCG) are two kinds of essential elements. A PCC is a Boolean color comparison of two granules and a PCG is a Boolean gradient comparison of two granules, which is motivated by...
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