Chung-Hao Chen

Bio: Chung-Hao Chen is an academic researcher from Old Dominion University. The author has contributed to research in topics: Video tracking & Smart camera. The author has an hindex of 11, co-authored 48 publications receiving 448 citations. Previous affiliations of Chung-Hao Chen include North Carolina Central University & University of Tennessee.

Heterogeneous Fusion of Omnidirectional and PTZ Cameras for Multiple Object Tracking

Abstract: Dual-camera systems have been widely used in surveillance because of the ability to explore the wide field of view (FOV) of the omnidirectional camera and the wide zoom range of the PTZ camera. Most existing algorithms require a priori knowledge of the omnidirectional camera's projection model to solve the nonlinear spatial correspondences between the two cameras. To overcome this limitation, two methods are proposed: 1) geometry and 2) homography calibration, where polynomials with automated model selection are used to approximate the camera's projection model and spatial mapping, respectively. The proposed methods not only improve the mapping accuracy by reducing its dependence on the knowledge of the projection model but also feature reduced computations and improved flexibility in adjusting to varying system configurations. Although the fusion of multiple cameras has attracted increasing attention, most existing algorithms assume comparable FOV and resolution levels among multiple cameras. Different FOV and resolution levels of the omnidirectional and PTZ cameras result in another critical issue in practical tracking applications. The omnidirectional camera is capable of multiple object tracking while the PTZ camera is able to track one individual target at one time to maintain the required resolution. It becomes necessary for the PTZ camera to distribute its observation time among multiple objects and visit them in sequence. Therefore, this paper addresses a novel scheme where an optimal visiting sequence of the PTZ camera is obtained so that in a given period of time the PTZ camera automatically visits multiple detected motions in a target-hopping manner. The effectiveness of the proposed algorithms is illustrated via extensive experiments using both synthetic and real tracking data and comparisons with two reference systems.

Can You See Me Now? Sensor Positioning for Automated and Persistent Surveillance

Abstract: Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.

Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization

Abstract: In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.

Sensor planning for automated and persistent object tracking with multiple cameras

Abstract: Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, a fundamental requirement of object tracking, is insufficient for persistent and automated tracking. In such applications, a continuous and consistently labeled trajectory of the same object should be maintained across different cameraspsila views. Therefore, a sufficient overlap between the cameraspsila FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. The proposed sensor planning method improves existing algorithms by adding handoff rate analysis, which preserves necessary overlapped FOVs for an optimal handoff success rate. In addition, special considerations such as resolution and frontal view requirements are addressed using two approaches: direct constraint and adaptive weight. The resulting camera placement is compared with a reference algorithm by Erdem and Sclaroff. Significantly improved handoff success rate and frontal view percentage are illustrated via experiments using typical office floor plans.

A Moving Object Tracked by A Mobile Robot with Real-Time Obstacles Avoidance Capacity

Abstract: This paper describes a robotic application that tracks a moving object by utilizing a mobile robot with multiple sensors. The robotic platform uses a visual camera to sense the movement of the desired object and a range sensor to help the robot detect and then avoid obstacles in real time while continuing to track and follow the desired object. In terms of real-time obstacle avoidance capacity, this paper also presents a modified potential field algorithm called Dynamic Goal Potential Field algorithm (DGPF) for this robotic application specifically. Experimental results show that the robotic and intelligent system can fulfill the requirements of tracking an object and avoiding obstacles simultaneously when the object is moving.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Applied Linear Regression Models

Abstract: (1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Active vision in robotic systems: A survey of recent developments

Abstract: In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.