Adaptive Background Update Research Articles

Strong interference adaptive suppression algorithm for respiratory and heartbeat monitoring

A strong interference adaptive suppression algorithm based on frequency-modulated continuous wave (FMCW) radar is proposed for scenarios with complex static targets. The algorithm removes stationary clutter, stationary fixed targets, and random background noise contained in the echo signal through background resolution, background accumulation, background differencing, and adaptive background update and detects human targets using the cell-averaged selected small constant false alarm rate (SO-CFAR) algorithm. Experiments show that this method improves target detection accuracy in low SNR, reduces the impact of background removal on the target phase, and improves the accuracy of target respiration and heartbeat value estimation.

Motion Target Detection of Birds Based on Adaptive Background Update Mechanism

In view of the natural environment variability and uncertainty makes observations of some birds behavior complex and difficult, this paper proposes a Surendra adaptive background updating algorithm based on global threshold, which can update background automatically according to the environmental changes, and then extract the birds target region of interested by background subtraction and morphological filtering. The experimental results show that the birds moving target detection of video-captured has a good extraction effect.

Video Object Detection Algorithm Based on Gaussian Mixture Models

The moving object detection of the video image is the basis of sequence image analysis, and it is the research hot issue of today’s foreign and domestic scholars. For detecting the moving object from the scene image in time, a detection algorithm of video moving object based on Gaussian mixture models is proposed in the paper. The pixel values are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixel of the current frame determines whether the light has a larger change, and it combines with the frame-difference method to detect moving object. The experimental results show that the algorithm can quickly and accurately establish the background model and accurately segment the foreground object.

Moving vehicles detection based on adaptive motion histogram

As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results.

Content-based event retrieval using semantic scene interpretation for automated traffic surveillance

This paper proposes an object segmentation and tracking algorithm for visual surveillance applications. In order to detect moving objects from a dynamic background scene which may have temporal clutters such as swaying plants, we devised an adaptive background update method and a motion classification rule. A two-dimensional token-based tracking system using a Kalman filter is designed to track individual objects under occlusion conditions. We propose a new occlusion reasoning approach where we consider two different types of occlusion: explicit occlusion and implicit occlusion. By tracking individual objects with segmented data, we can generate motion trajectories and set a motion model using polynomial curve fitting. The trajectory model is used as an indexing key for accessing the individual object in the semantic level. We also propose an efficient way of indexing and searching based on object-specific features at different semantic levels. The proposed searching scheme supports various queries including query by example, query by sketch, and query on weighting parameters for event-based retrieval. When retrieving an interested video clip, the system returns the best matching event in the similarity order. In addition, we implement a temporal event graph for direct accessing and browsing of a specific event in the video sequence.