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In intelligent surveillance field, the numerous methods have been proposed for foreground extraction from a stationary or dynamic background from a general video sequence. It is very difficult for the proposed methods to have both high accuracy, low computational complexity and less memory requirements. Unlike previous approaches to object detection which detect objects by building adaptive models...
In this paper an improved real time algorithm for detecting pedestrians in surveillance video is proposed. The algorithm is based on people appearance and defines a person model as the union of four models of body parts. Firstly, motion segmentation is performed to detect moving pixels. Then, moving regions are extracted and tracked. Finally, the detected moving objects are classified as human or...
This paper proposes a semi-supervised intelligent visual surveillance system to exploit the information from multi-camera networks for the monitoring of people and vehicles. Modules are proposed to perform critical surveillance tasks including: the management and calibration of cameras within a multi-camera network; tracking of objects across multiple views; recognition of people utilising biometrics...
A new method for real-time detection and tracking of multiple moving vehicles from traffic video is proposed. This method first uses MoG and texture based model to extract foreground from the scene, then detect moving targets using a modified version of timed motion history image (tMHI), and finally uses Kalman prediction filter to track these targets, which the full moving trajectories of the targets...
In this paper, we describe a new dense spatio-temporal motion segmentation algorithm with application to tracking of people in crowded environments. The algorithm is based on state-of-the-art motion and image segmentation algorithms. We specifically make use of a mean shift image segmentation algorithm and two graph based motion segmentation algorithms. The resulting motion segmentation is on the...
Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We develop an efficient adaptive segmentation algorithm for color video surveillance sequence in real time with non-stationary background; background is modeled using partial correlation coefficient using pixel-level based approach. At runtime, segmentation is performed by checking...
We designed a real time moving object extraction and behavior analysis system to detect if any abnormal behavior like intrusion, halt, and wall climbing happened. Much work in the area of motion based video object segmentation is being done in the pixel domain which exploits the visual attributes and motion information. Given most existing images and videos stored in the compressed form, the specific...
In this paper we present a real-time object tracking system for monocular video sequences with static camera. The workflow is based on a pixel-based foreground detection system followed by foreground object tracking. The foreground detection method performs the segmentation in three levels: Moving Foreground, Static Foreground and Background level. The tracking uses the foreground segmentation for...
Active contour model and mean shift are both motion detection algorithms. Each of them has its own merits and shortcomings. An active contour tends to be tracked by noise points and results in a false boundary. A mean shift vector always points to the edge area when the start point is around the object With initial curves given near the objects in each image automatically, we presented a new motion...
In this paper, we designed a simple and fast visual surveillance system to track human position and to determine if any abnormal behavior like wall climbing and falling happened. By taking both time and background difference into considerations, illumination effects could be greatly reduced while calculating motion masks. Refinements including holes filling, shadow removal, and noise reduction are...
For many intelligent security systems the use of infrared technology is becoming essential and is a challenging issue. This paper outlines a framework for exploiting spatio-temporal tracking parameters to classify multiple moving objects and recognize their interactions using low quality thermal imagery. For outdoor scenes, motion segmentation is automatically performed using a novel dynamic background-subtraction...
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