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Pedestrian detection is one of the most popular research areas in video processing and it is vital for video surveillance systems. In this paper, we present a real-time pedestrian detection system based on Dalal and Triggs's human detection framework with the use of image segmentation and virtual mask. Image segmentation enables the system to focus only on the region of interest whereas the virtual...
For enlarging the surveillance area, more and more visual surveillance systems exploit Pan Tilt Zoom (PTZ) camera. This paper proposes a framework of surveillance system which uses a single PTZ camera. The framework is divided into two phases: offline phase and inline phase. During the offline phase, camera parameters for every image are computed using SIFT features and bundle adjustment algorithm,...
In this study, a calibrated dual-camera device, a fixed camera and a pan-tilt-zoom camera, is setup to monitor moving vehicles in an open space. This device not only tracks multiple targets but also gets the license plate images with high quality. Next, a convolutional neural network (CNN) is designed to be a detector and a character classifier for efficiently locating the regions of license plates...
We present a machine learning approach to detect changes in human appearance between instances of the same person that may be taken with different cameras, but over short periods of time. For each video sequence of the person, we approximately align each frame in the sequence and then generate a set of features that captures the differences between the two sequences. The features are the occupancy...
We present a framework for detecting and recognizing human activities for outdoor video surveillance applications. Our research makes the following contributions: For activity detection and tracking, we improve robustness by providing intelligent control and fail-over mechanisms, built on top of low-level motion detection algorithms such as frame differencing and feature correlation. For activity...
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