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IRIS Research Staff |
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Chung-Hao Chen |
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Office: |
334 Ferris Hall |
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Telephone: |
(865) 974-5767 |
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Fax: |
(865) 974-5459 |
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E-mail: |
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Current Work:
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Reliability Assessment for Automated Video Surveillance Systems Most existing performance evaluation methods concentrate on defining separate metrics over a wide range of conditions and generating standard benchmarking video sequences for examining the effectiveness of video tracking systems. In other words, their methods are to design a robustness margin or factor into the system. This is a deterministic method in which a robustness factor such as 2 or 3 times the expected number of objects or the strength of illumination would be allowed for in the design. This often results in overdesign, thus increasing costs, or in underdesign, causing failure by unanticipated factors. In order to overcome its limitations, in this paper we propose an alternative framework to analyze the physics of the failure process and, through the concept of reliability, determine the time to failure in automated video surveillance systems. The advantage of our proposed framework is that we can provide a unified and statistical index to evaluate the performance of automated video surveillance systems Meantime, based on our proposed framework, the uncertainty problem about a failure process, which is caused by the system’s complexity, imprecise measurements of the relevant physical constants and variables, and the indeterminate nature of future events can be addressed accordingly. . |
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Homogeneous Fusion and Coordination of Multiple PTZ Cameras in Automated Surveillance Systems Due to the capacity of pan-tilt-zoom (PTZ) cameras to simultaneously cover a panoramic area and maintain high resolution imagery, researches in surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of the PTZ camera’s intrinsic parameters to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of PTZ camera’s intrinsic parameters and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy, as compared with the most existing works. . |
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Camera Handoff for Automated Tracking Systems with Multiple Omnidirectional Cameras In a multi-camera surveillance system, both camera handoff and placement play an important role in generating an automated and persistent object tracking, typical of most surveillance requirements. Camera handoff should comprise three fundamental components, time to trigger handoff process, the execution of consistent labeling, and the selection of the next optimal camera. In this paper, we design an observation measure to quantitatively formulate the effectiveness of object tracking so that we can trigger camera handoff timely and select the next camera appropriately before the tracked object falls out of the field of view (FOV) of the currently observing camera. In the meantime, we present a novel solution to the consistent labeling problem in omnidirectional cameras. A spatial mapping procedure is proposed to consider both the noise inherent to the tracking algorithms used by the system and the lens distortion introduced by omnidirectional cameras. This does not only avoid the tedious process, but also increases the accuracy, to obtain the correspondence between omnidirectional cameras without human interventions. |
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Heterogeneous Fusion of Omnidirectional and PTZ Cameras for Multiple Object Tracking
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 non-linear spatial correspondences between the two cameras. To overcome this limitation, two methods are proposed: (a) geometry and (b) 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. |
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Wireless Network Development and System Integration for the Multi-Sensors Single Robot Platform
The purpose of this project is to achieve a multi-platform integration demo. In our multi-sensors single robot system, the Robot's operating system is Windows XP and the Sensor's operating system of each Sensor could be Windows XP, Windows CE, Pocket PC, or Linux. In the first step, we have to investigate relevant development environments, wireless transmission technology, and integration interface among bricks. Secondly, we have to import indicated clients to Linux, Pocket PC, or Windows CE. Finally, we have to integrate it with real sensor bricks and show the full system demo. |
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