Norimichi Ukita

Real-time multitarget tracking by a cooperative distributed vision system

Target detection and tracking is one of the most important and fundamental technologies to develop real-world computer vision systems such as security and traffic monitoring systems. This paper first categorizes target tracking systems based on characteristics of scenes, tasks, and system architectures. Then we present a real-time cooperative multitarget tracking system. The system consists of a group of active vision agents (AVAs), where an AVA is a logical model of a network-connected computer with an active camera. All AVAs cooperatively track their target objects by dynamically exchanging object information with each other With this cooperative tracking capability, the system as a whole can track multiple moving objects persistently even under complicated dynamic environments in the real world. In this paper we address the technologies employed in the system and demonstrate their effectiveness.

Articulated pose estimation with parts connectivity using discriminative local oriented contours

This paper proposes contour-based features for articulated pose estimation. Most of recent methods are designed using tree-structured models with appearance evaluation only within the region of each part. While these models allow us to speed up global optimization in localizing the whole parts, useful appearance cues between neighboring parts are missing. Our work focuses on how to evaluate parts connectivity using contour cues. Unlike previous works, we locally evaluate parts connectivity only along the orientation between neighboring parts within where they overlap. This adaptive localization of the features is required for suppressing bad effects due to nuisance edges such as those of background clutter and clothing textures, as well as for reducing computational cost. Discriminative training of the contour features improves estimation accuracy more. Experimental results verify the effectiveness of our contour-based features.

Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints

We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-fitting clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-fitting clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.

Wearable virtual tablet: fingertip drawing on a portable plane-object using an active-infrared camera

We propose the Wearable Virtual Tablet (WVT), where a user can draw a locus on a common object with a plane surface (e.g., a notebook and a magazine) with a fingertip. Our previous WVT[1], however, could not work on a plane surface with complicated texture patterns: Since our WVT employs an active-infrared camera and the reflected infrared rays vary depending on patterns on a plane surface, it is difficult to estimate the motions of a fingertip and a plane surface from an observed infrared-image. In this paper, we propose a method to detect and track their motions without interference from colored patterns on a plane surface. (1) To find the region of a plane object in the observed image, four edge lines that compose a rectangular object can be easily extracted by employing the properties of an active-infrared camera. (2) To precisely determine the position of a fingertip, we utilize a simple finger model that corresponds to a finger edge independent of its posture. (3) The system can distinguish whether or not a fingertip touches a plane object by analyzing image intensities in the edge region of the fingertip.

3D Scene Reconstruction from Reflection Images in a Spherical Mirror

This paper proposes a method for reconstructing a 3D scene structure by using the images reflected in a spherical mirror. In our method, the mirror is moved freely within the field of view of a camera in order to observe a surrounding scene virtually from multiple viewpoints. The observation scheme, therefore, allows us to obtain the wide-angle multi-viewpoint images of a wide area. In addition, the following characteristics of this observation enable multi-view stereo with simple calibration of the geometric configuration between the mirror and the camera; (1) the distance and direction from the camera to the mirror can be estimated directly from the position and size of the mirror in the captured image and (2) the directions of detected points from each position of the moving mirror can be also estimated based on reflection on a spherical surface. Some experimental results show the effectiveness of our 3D reconstruction method

Real-time multi-target tracking by cooperative distributed active vision agents

This paper presents a real-time cooperative multi-target tracking system. The system consists of a group of Active Vision Agents (AVAs), where an AVA is a logical model of a network-connected computer with an active camera. All AVAs cooperatively track their target objects by dynamically interacting with each other. As a result, the system as a whole can track multiple moving objects simultaneously under complicated dynamic situations in the real world. To implement the real-time cooperation among AVAs, we designed a three-layered interaction architecture. In each layer, parallel processes mutually exchange various information for the effective cooperation. To realize the information exchange in real time, we employed the dynamic memory architecture[6]. Experimental results demonstrate that AVAs cooperatively track their target objects in real-time while adaptively changing their roles.

Estimation of 3D gazed position using view lines

We propose a new wearable system that can estimate the 3D position of a gazed point by measuring multiple binocular view lines. In principle, 3D measurement is possible by the triangulation of binocular view lines. However, it is difficult to measure these lines accurately with a device for eye tracking, because of errors caused by (1) difficulty in calibrating the device and (2) the limitation that a human cannot gaze very accurately at a distant point. Concerning (1), the accuracy of calibration can be improved by considering the optical properties of a camera in the device. To solve (2), we propose a stochastic algorithm that determines a gazed 3D position by integrating information of view lines observed at multiple head positions. We validated the effectiveness of the proposed algorithm experimentally.

Multiple Active Camera Assignment for High Fidelity 3D Video

We are designing a self controlling active camera system for a 3D video of a moving object (mainly human body). We made up our system of cameras with long focal length lenses for high resolution input images. However, such cameras can get only partial views of the object. We present, in this paper, a multiple active (pan-tilt) camera assignment scheme. The goal is to assign each camera to a specific part of the moving object so as to allow the best visibility of the whole object. For each camera, we evaluate the visibility to the different regions of the object, corresponding to different camera orientations and with respect to the field of view of the camera in question. Thereafter, we assign each camera to one orientation in such a way to maximize the visibility to the whole object.

Real-time cooperative multi-target tracking by communicating active vision agents

W e present a real-time cooperative multitarget tracking system. The system consists of a group of Active Vision Agents (AVAs), where an AVA i s a logical model of a network-connected computer with a n active camera. All AVAs cooperatively track their target objects by dynamically exchanging object i n f o m a tion with each other. I n this paper, we address the technologies employed in the system and demonstrate their effectiveness.

Tandem Equipment Arranged Architecture with Exhaust Heat Reuse System for Software-Defined Data Center Infrastructure

In this paper, we propose a novel energy-efficient architecture for software-defined data center infrastructures. In our proposed data center architecture, we include an exhaust heat reuse system that utilizes high-temperature exhaust heat from servers in conditioning humidity and air temperature of office space near the data center. To obtain high-temperature exhaust heat, equipment such as server racks and air conditioners are deployed in tandem so that the aisles are divided into three types: cold, hot, and super-hot. In this paper, to investigate the fundamental characteristics of our proposed data center architecture, we consider various types of data center models and conduct numerical simulations that use results obtained by experiments at an actual data center. Through simulation, we show that the total power consumption by a data center with our proposed architecture is 27 percent lower than that by data center with a conventional architecture. In addition, it is also shown that the proposed tandem equipment arrangement is suitable for obtaining high-temperature exhaust heat and decreasing the total power consumption significantly under a wider range of conditions than in the conventional equipment arrangement.