In this paper, an improved ViBe background subtraction algorithm is proposed for dealing with ghost problem during the process of moving object detection. The ghost areas in image can be detected based on the theory that the histogram distributions of ghost areas have similarity distribution characteristics. However, the histogram distributions of real moving objects change with the real objects moving. The influence of ghost areas on moving object detection is eliminated. The improved ViBe, the original ViBe and the Gauss mixture model are compared and analyzed, The results show that the improved ViBe can effectively eliminate ghost areas, and has high real-time.

So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural network with an attention mechanism to recognize the detected motion object, thus eliminating the interference motion and increasing the detection accuracy rate. Experimental results on the artificial data and relevant scene data show that the proposed detection method can effectively identify the slope collapse, which has its applicative value and brilliant prospect.

In this paper, we study a task of slope collapse detection (SCD) for river embankment and formulate it as the tasks of motion detection and image recognition. Specifically, we introduce an SCD method based on motion detection and image recognition technologies to help inspector attendants detect the slope collapse. In this method, we use the foreground motion detection algorithm to identify the slope collapse of the scene of the river embankment. Since the moving targets in the foreground may not only be the slope collapse but also maybe some biology, we further use the image feature extraction and image recognition technology to recognize the foreground motion area, thus eliminating the influence of the biology on the detection results. Experimental results on the relevant scene data show that the proposed method can identify the slope collapse in real-time, and can effectively eliminate the motion interference of the biology, which has a high practical value.

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