A surveillance system that detects and tracks security breaches in airports is presented. The system consists of two subsystems with one overhead static and one Pan/Tilt/Zoom (PTZ) camera to first acquire and then follow an intruder who illegally walks into a crowded secure area of an airport. The overhead camera detects the intruder using a motion-based segmentation and an optical flow algorithm. Intruder handover from the overhead camera to the PTZ camera is then performed. A novel approach for intruder handover and feature extraction using color is presented for continuous tracking with the PTZ camera when the intruder moves out of the view of the overhead camera. We also use a mean shift filter with a newly designed non-rectangular search window which will be automatically updated to accurately localize the target. Real experimental results from a local airport are given and discussed.
Automatic tracking is essential for a 24 hours intruder-detection and, more generally, a surveillance system. This paper presents an adaptive background generation and the corresponding moving region detection techniques for a Pan-Tilt-Zoom (PTZ) camera using a geometric transform-based mosaicing method. A complete system including adaptive background generation, moving regions extraction and tracking is evaluated using realistic experimental results. More specifically, experimental results include generated background images, a moving region, and input video with bounding boxes around moving objects. This experiment shows that the proposed system can be used to monitor moving targets in widely open areas by automatic panning and tilting in real-time.
This paper presents a new method for seamless tracking a moving object by using multiple fixed cameras controlled by a single processor. For seamless tracking a moving object with multiple cameras, we should extract important features of the object from input images captured by multiple cameras. Because two adjacent cameras, in general, have different parameters, such as: zooming ratio, color distribution, and direction of view, we use the color information of overlapped region and feature vector, such as object's ratio and moving direction. We first investigate the distribution of color component in the overlapped region. We then use the results to discriminate the target object and to hand over the current view. Experimental results show the feasibility of the proposed seamless tracking method and its real-time applications.
Recently, many image processing systems are required to offer high-quality images. For example, when we use a surveillance system with a digital camcorder and a digital video recorder, it is highly probable that the acquired image suffers from various image degradation, such as motion blur and out-of- focus blur. With such degradation, we cannot obtain important information. This is mainly cased by limited performance of image formation system. In this work, we investigate the causes of focus blur and motion blur. With the simultaneous formulation of the corresponding degradation, we propose a spatially adaptive regularization algorithm for restoring out- of-focus and motion blurred images. Accordingly, we present a method to estimate blur parameters and a segmentation method for spatially adaptive processing.
We proposed a new degradation model for auto focus blur between multiple objects with different out of focus parameters and the segmentation-based spatially adaptive regularized iterative restoration algorithm. In the proposed model, the boundary effect of out of focus objects is mathematically analyzed. By using experimental results, we show that the proposed restoration alignment can efficiently remove the space-variant out of focus blur of multiple blurred objects image.
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