Abstract
In video surveillance, there are many interference factors such as target changes, complex scenes, and target deformation in the moving object tracking. In order to resolve this issue, based on the comparative analysis of several common moving object detection methods, a moving object detection and recognition algorithm combined frame difference with background subtraction is presented in this paper. In the algorithm, we first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and then get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average. In this case, weight of object information has been increasing, and also restrains the static background. Eventually the motion detection image contains both the target contour and more target information of the target contour point from the background image, so as to achieve separating the moving target from the image. The simulation results show the effectiveness of the proposed algorithm.
Highlights
Compared with the relative static image, the movement image contains more information, and we can extract the contained information from the movement image by means of image processing
We first calculate the average of the values of the gray of the continuous multi-frame image in the dynamic image, and get background image obtained by the statistical average of the continuous image sequence, that is, the continuous interception of the N-frame images are summed, and find the average
Considering the complexity of the environment during the image acquisition process, the quality of moving object detection depends on the following characteristics: ability to adapt to changes in ambient light, ability to adapt to maintain good results in a variety of weather conditions, ability to avoid interference from detecting similar fluctuations and jitter exists in the scene, ability to accurately identify the erratic movement of large areas, and the change of the object’s quantity in the movement scene
Summary
Compared with the relative static image, the movement image contains more information, and we can extract the contained information from the movement image by means of image processing. The purpose of moving object detection is to extract the required information from the image. Considering the complexity of the environment during the image acquisition process, the quality of moving object detection depends on the following characteristics: ability to adapt to changes in ambient light, ability to adapt to maintain good results in a variety of weather conditions, ability to avoid interference from detecting similar fluctuations and jitter exists in the scene, ability to accurately identify the erratic movement of large areas, and the change of the object’s quantity in the movement scene. The paper first introduces the principle of frame difference and background subtraction, and researched the common two-frame difference and three-frame difference [1], lastly put forward a background subtraction based on codebook model and Bayesian classification
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