The Flocs Target Detection Algorithm Based on the Three Frame Difference and Enhanced Method of the Otsu

The process of missile launch training was confined to the virtual scene in the past. So, cooperating with an artillery college, the group makes the moving target detection technology to be applied in missile training equipment, so as to make the training apply to the field operations. This paper presents the frame difference mapping algorithm, which is used to detect the moving target in the background of moving video frame. According to the target region which is given out by the system in the graphical interface, the students do the launching missile training. The moving target detection algorithm which is provided with the low complexity and the high accuracy, i.e. proposed by the paper, is based on Gauss mixture model and frame difference mapping. The mechanism of layered-graphics and the message agent which makes the modules in the system be independent of each other are used in the system designing. So, the module coupling degree in terms of this mechanism is lower than before. This mechanism brings convenience to system maintenance and upgrade, especially for the system’s transplanting to the real missile launch system in future.

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Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

Contrast Media & Molecular Imaging ◽

10.1155/2021/3666622 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Renzheng Xue ◽

Ming Liu ◽

Xiaokun Yu

Keyword(s):

Target Detection ◽

Difference Method ◽

Moving Objects ◽

Target Location ◽

Mean Shift ◽

Detection Algorithm ◽

Moving Target ◽

Size Change ◽

Detection And Tracking ◽

Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

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A Human Target Detection and Tracking Method Based on Adaptive Difference and GVF-Snake Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.455.344 ◽

2013 ◽

Vol 455 ◽

pp. 344-349

Author(s):

Xue Zhang ◽

Wei Cheng Xie ◽

Chao Huang ◽

Qiang Xu

Keyword(s):

Target Detection ◽

Detection Algorithm ◽

Initial Contour ◽

Differential Detection ◽

Body Contour ◽

Snake Model ◽

Tracking Method ◽

Detection And Tracking ◽

Frame Difference ◽

Snake Algorithm

Detection of Frame difference fails when the human target is stationary in course of moving, this paper presents a method based on combination of adaptive difference and GVF-snake algorithm to solve it. Adaptive differential detection algorithm can accurately extract the target contour, and use it as the initial contour of GVF-snake model which cannot automatically extract it after we got the target. In the process of detection and tracking, calculating GVF field of the whole picture consume too much time, so we use the method of sub-region to improve the real-time. The experimental results show that, the algorithm can provide the actual body contour for GVF-snake model, and effectively track whether the target is stationary or moving.

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