A Sensor-based SLAM Algorithm for Camera Tracking in Virtual Studio

This paper addresses a sensor-based simultaneous localization and mapping （SLAM） algorithm for camera tracking in a virtual studio environment. The traditional camera tracking methods in virtual studios are vision-based or sensor-based. However, the chroma keying process in virtual studios requires color cues, such as blue background, to segment foreground objects to be inserted into images and videos. Chroma keying limits the application of vision-based tracking methods in virtual studios since the background cannot provide enough feature information. Furthermore, the conventional sensor-based tracking approaches suffer from the jitter, drift or expensive computation due to the characteristics of individual sensor system. Therefore, the SLAM techniques from the mobile robot area are first investigated and adapted to the camera tracking area. Then, a sensor-based SLAM extension algorithm for two dimensional （2D） camera tracking in virtual studio is described. Also, a technique called map adjustment is proposed to increase the accuracy＇ and efficiency of the algorithm. The feasibility and robustness of the algorithm is shown by experiments. The simulation results demonstrate that the sensor-based SLAM algorithm can satisfy the fundamental 2D camera tracking requirement in virtual studio environment.

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

Survey of 3D modeling using depth cameras

Three-dimensional(3D)modeling is an important topic in computer graphics and computer vision.In recent years,the introduction of consumer-grade depth cameras has resulted in profound advances in 3D modeling.Starting with the basic data structure,this survey reviews the latest developments of 3D modeling based on depth cameras,including research works on camera tracking,3D object and scene reconstruction,and high-quality texture reconstruction.We also discuss the future work and possible solutions for 3D modeling based on the depth camera.

Ob ject Tracking with Dual Field-of-view Switching in Aerial Videos

Visual object tracking plays an important role in intelligent aerial surveillance by unmanned aerial vehicles（UAV）. In ordinary applications, aerial videos are captured by cameras with a fixed-focus lens or a zoom lens, for which the field-of-view（FOV）of the camera is fixed or smoothly changed. In this paper, a special application of the visual tracking in aerial videos captured by the dual FOV camera is introduced, which is different from ordinary applications since the camera quickly switches its FOV during the capturing. Firstly, the tracking process with the dual FOV camera is analyzed, and a conclusion is made that the critical part for the whole process depends on the accurate tracking of the target at the moment of FOV switching. Then, a cascade mean shift tracker is proposed to deal with the target tracking under FOV switching. The tracker utilizes kernels with multiple bandwidths to execute mean shift locating, which is able to deal with the abrupt motion of the target caused by FOV switching. The target is represented by the background weighted histogram to make it well distinguished from the background, and a modification is made to the weight value in the mean shift process to accelerate the convergence of the tracker. Experimental results show that our tracker presents a good performance on both accuracy and efficiency for the tracking. To the best of our knowledge, this paper is the first attempt to apply a visual object tracking method to the situation where the FOV of the camera switches in aerial videos.