Abstract
This paper presents a moving-object segmentation algorithm using edge information as segment. The proposed method is developed to address challenges due to variations in ambient lighting and background contents. We investigated the suitability of the proposed algorithm in comparison with the traditional-intensity-based as well as edge-pixel-based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and moving-object segmentation; and facilitates incorporating knowledge into edge segment during background modeling and motion tracking. An efficient approach for background initialization and robust method of edge matching is presented, to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. Detected moving edges are utilized along with watershed algorithm for extracting video object plane (VOP) with more accurate boundary. Experiment results with real image sequence reflect that the proposed method is suitable for automated video surveillance applications in various monitoring systems.
Highlights
Moving object segmentation is of important research interest for widespread applications in diverse disciplines
The segmentation in automated video surveillance isolates the events of potential interest from a large volume of redundant image data, since human observers may be distracted from this task
A basic motion detection algorithm takes in an image sequence as input, detects frames having significant change from the previous frames or background image and extracts the significantly changed regions [1]
Summary
Moving object segmentation is of important research interest for widespread applications in diverse disciplines. Traditional edge pixel-based moving object detection methods do not represent edge information using any data structure and they need visiting all the image location to access edge points [8,9,10]. These methods treat each edge point independently which is not convenient for matching and tracking. Detection and update can be performed by using edge list, without accessing input frame This representation helps to incorporate an efficient and flexible edge matching algorithm.
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