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In this paper we propose an approach to recognize human actions using depth images. Here, we capture the motion dynamics of the object from the depth difference image and average depth image. The features from the space-time depth difference images are obtained from hierarchical division of the silhouette bounding box. We also make use of motion history images to represent the temporal information...
Falls are a major threat to the independence and quality of life of elderly people. As the worldwide population of elderly increases each year, responding to falls is essential. Computer vision systems provide a new promising solution in responding falls through detecting fall events. This paper presents a new technique in detecting falls based on human shape variation. The proposed visual based fall...
We investigate pedestrian detection in depth images. Unlike pedestrian detection in intensity images, pedestrian detection in depth images can reduce the effect of complex background and illumination variation. We propose a new feature descriptor, Histogram of Depth Difference(HDD), for this task. The proposed HDD feature descriptor can describe the depth variance in a local region as Histogram of...
Video analysis aiming at efficient pedestrian detection is an important research area in computer vision and robotics. Although this is a well studied topic, successful detection still remains a challenge in outdoor, low resolution images. We present efficient detection metrics which consider the fact that human movement presents some characteristic patterns. Unlike many methods which perform an intra-blob...
We introduce an action recognition approach based on Partial Least Squares (PLS) and Support Vector Machines (SVM). We extract very high dimensional feature vectors representing spatio-temporal properties of actions and use multiple PLS regressors to find relevant features that distinguish amongst action classes. Finally, we use a multi-class SVM to learn and classify those relevant features. We applied...
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...
Summary form only given. Human-computer interfaces (HCI) have evolved from mouse-keyboard based interaction using text and mouse events to multi-touch screens and other exotic approaches such as using special gloves or other devices to translate human actions into application controls. One of the trends which are “en vogue” in our days is the control of computer applications and/or computer controlled...
Human detection is a key functionality to reach Human Robot/Computer Interaction. The human tracking is also a rapidly evolving area in computer and robot vision; it aims to explore and to follow human motion. We present in this article an intelligent system to learn human detection. The descriptors used in our system make up the combination of HOG and SIFT that capture salient features of humans...
This paper proposes a technique for 3-D recovery of a non-rigid object, such as a moving person, from a single camera view. Recovery of a non-rigid object is not possible from a single camera view without any condition. In this paper, we propose a single camera technique for recovering a non-rigid object under the condition that the object is composed of a set of rigid objects. The experiments employing...
Human action recognition is an important research area in the field of computer vision having a great number of real-world applications. This paper presents a multi-view action recognition framework that extracts human silhouette clues from different cameras, analyzes scene dynamics and interprets human behaviors by the integration of multivariate data in fuzzy rule-based system. Different features...
In this paper, we explore a new approach for enriching the HoG method for pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on using gait motion since the rhythmic footprint pattern for walking people is considered the stable and characteristic feature for the detection of walking people. The novelty of our approach is motivated by the latest research for...
3D reconstruction has been widely used in many important applications. While extensive research has been done in 3D reconstruction, several key issues are still open and the precision of the recovered regions is still far from satisfaction. In this paper, we propose a novel approach to selecting regions of interest in video frames by analyzing multiple spatio-temporal characteristics and reconstructing...
Current research on visual action/activity analysis has mostly exploited appearance-based static feature descriptions, plus statistics of short-range motion fields. The deliberate ignorance of dense, long-duration motion trajectories as features is largely due to the lack of mature mechanism for efficient extraction and quantitative representation of visual trajectories. In this paper, we propose...
This paper describes an algorithm enabling a human supervisor to convey task-level information to a robot by using stylus gestures to circle one or more objects within the field of view of a robot-mounted camera. These gestures serve to segment the unknown objects from the environment. Our method's main novelty lies in its use of appearance-based object “reacquisition” to reconstitute the supervisory...
Human, especially human-of-interest, detection and tracking is a necessary part for immersive and realistic human-computer interaction. Thus, we propose an intelligent multi-vision-based human detection and tracking system that consists of a visible camera, an infrared camera, a dynamic infrared lighting system, and a cold mirror. The system segments a human region of interest and tracks his/her motion...
This paper presents a method for automated human detection using fisheye lens camera. We introduce a probabilistic model to describe the wide variation of human appearance in hemispherical image. In our method, a human is modeled as probabilistic shape features of body silhouette and head-shoulder contour. These features are extracted from the human images taken at various distance and orientation...
We perform action recognition with a robust approach to recognize action information based on explicitly encoding motion information. This algorithm detects interest points and encodes not only their local appearance but also explicitly models local motion. Our goal is to recognize individual human actions in the operations of a home medical device to see if the patient has correctly performed the...
This paper proposes a technique for recovering 3-D shape of a non-rigid object employing a single camera. Normally two or more cameras are necessary for recovering 3-D shape of a non-rigid object. Shape recovery of a non-rigid object employing a single camera has recently been studied by a few researchers, but they deal only with small deformation. The technique proposed in this paper achieves the...
Human motion recognition is traditionally approached by either recognizing basic motions from features derived from video input or by interpreting complex motions by applying a high-level hierarchy of motion primitives. The former method is usually limited to rather simple motions while the latter requires human expert knowledge to build up a suitable hierarchy. In this paper we propose a new approach...
In this paper, a robust and efficient approach for multicamera human tracking is presented. The approach is integrated in an experimental surveillance system, based on a camera network with a task-oriented architecture. At sensor level, image processing algorithms are applied for object detection and feature extraction. Additionally, for each object that is to be tracked, an agent-based multi-sensor...
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