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In the human-robot interactions, it is an important research on the artificial intelligence that robots catch the human's attention information. This process has a significant impact on robot intelligence. In this paper, a system for extracting human's attention-information from interaction has been designed. On the basis of face detection and eye-area location, an algorithm, combined with ant colony...
In this paper, we propose a framework that fuses global and local features for action recognition in videos sequences. The combination of multiple features is important as single feature based method is not able to capture imaging variation (illumination changes, view point orientation etc. and attributes of individuals (age, size etc.). Hence, we use two types of features: i) a quantized local spatial-temporal...
The automatic analysis and understanding of behavior and interactions is a crucial task in the design of socially intelligent video surveillance systems. Such an analysis often relies on the extraction of people behavioral cues, amongst which body pose and head pose are probably the most important ones. In this paper, we propose an approach that jointly estimates these two cues from surveillance video...
In this paper we discuss several methods for the creation of 3D models that can provide additional information to robot operators in order to improve their situation awareness of the robot being teleoperated. We derive the 3D models from spatial data gathered from an inexpensive, readily available, video game sensor. In addition, the paper introduces a new method for feature extraction as part of...
In this paper, we present a part model for human action recognition from video. We use 3D HOG descriptor and bag-of-feature to represent video. To overcome the unordered events of bag-of-feature approach, we propose a novel multiscale local part model to preserve temporal context. Our method builds upon several recent ideas including dense sampling, local spatial-temporal (ST) features, 3D HOG descriptor,...
3D face reconstruction is a research hotspot in computer animation, computer games, computer vision, and image recognition fields in recent years. It can enhance the virtual reality experience. In this paper, the approach of 3D face reconstruction from one image is introduced. First, the multiple transformation modeling technique is used to construct a human face grid, which can be adjusted. Secondly,...
In this paper we present representations and mechanisms that facilitate continuous learning of visual concepts in dialogue with a tutor and show the implemented robot system. We present how beliefs about the world are created by processing visual and linguistic information and show how they are used for planning system behaviour with the aim at satisfying its internal drive - to extend its knowledge...
Recognizing human activities from common color image sequences faces many challenges, such as complex backgrounds, camera motion, and illumination changes. In this paper, we propose a new 4-dimensional (4D) local spatio-temporal feature that combines both intensity and depth information. The feature detector applies separate filters along the 3D spatial dimensions and the 1D temporal dimension to...
We present a Z-SIFT based 3D surface registration algorithm that utilizes the depth information enhanced SIFT features to make initial alignment and the 2D feature weighted Iterative Closest Point (ICP) algorithm to realize accurate registration. The combination of SIFT features and depth information extracts faithful corresponding points between the 2D images and provides good coarse alignment for...
In this paper we propose a framework for activity recognition based on space-time interest point in video surveillance. Single type interest point feature is not sufficient to identify the activity therefore we have considered multi-class activities fussed in three dimensional (spatial & time) coordinate to achieve our objective with maximum accuracy. Our experiment shows that fusing multi class...
In this paper, we propose a 3D template-based human action detection base on volume pattern matching. A volume pattern is obtained by detecting the principal plane from a space-time patch using the 3D moment-preserving technique. Instead of segmentation and detailed shape representation, the objective of this research is to develop and apply computer vision methods that explore the structure of a...
Using perception in the context of rendering is a wide spread field. It can be used to speed up calculations or create more detailed images by refining important areas. Saliency, as a perception based method, can identify regions of interest, which should contain more detail. But currently no complete mapping of the 2D operators to an 3D equivalent has been defined. We propose a Bidirectional Saliency...
The cerebral cortex of the human brain is highly folded. It is useful for neuroscientists and clinical researchers to identify and/or quantify cortical folding patterns across individuals. The top (gyri) and bottom (sulci) of these folds resemble the “blob-like” features used in computer vision. In this article, we evaluate different blob detectors and descriptors on brain MR images, and introduce...
Human actions are diversified and complicated, and hence difficult to be recognized by artificial intelligence systems. Recognizing actions in broadcast videos is even more challenging due to low resolution and frame rate. During the past decades, many talented researchers are devoted to this field, and several promising algorithms are developed to achieve great performance. The well known methods...
In this paper we explore the robustness of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the Civilian American and European Surface Anthropometry Resource Project (CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International)...
The paper presents an active vision system for human posture recognition, which is an important function of any assisted living system, suitable to be employed in indoor environments. Both hardware and software architectures are defined in order to meet constraints typically imposed by AAL (Ambient Assisted Living) contexts such as compactness, low-power consumption, installation simplicity, privacy...
Conventional human detection is mostly done in images taken by visible-light cameras. These methods imitate the detection process that human use. They use features based on gradients, such as histograms of oriented gradients (HOG), or extract interest points in the image, such as scale-invariant feature transform (SIFT), etc. In this paper, we present a novel human detection method using depth information...
In this paper an algorithmic framework for posture analysis using a single view 3D TOF camera is presented. The 3D human posture parameters are recovered automatically from range data without the usage of body markers. A topological approach is investigated in order to define descriptors suitable to estimate location of body parts and orientation of body articulations. Two Morse function are exploited,...
People detection and tracking is a key component for robots and autonomous vehicles in human environments. While prior work mainly employed image or 2D range data for this task, in this paper, we address the problem using 3D range data. In our approach, a top-down classifier selects hypotheses from a bottom-up detector, both based on sets of boosted features. The bottom-up detector learns a layered...
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