Extraction of Moving Objects on Underwater Video Using Method of Subtraction the Background Modeling Results

Abstract---This paper proposes a method for extracting moving objects on an underwater surveillance video. Video obtained using an underwater camera to capture the environmental conditions of the area. This research is the initial stage of the underwater surveillance system. Underwater surveillance system enables objects passing can be recognized shapes, types, and its behavior. The extraction method used in this research is a subtraction between the current frames with the background modeling results. Underwater video retrieval has a high level of difficulty because the background is always changing either due to a change the intensity and the movement of water currents. Therefore, it needs to be made an appropriate background model to address this problem. Modeling of the background on this research using adaptive modeling method, where the intensity of the background pixels is updated based on inference of the background intensity before. If the intensity of the pixels changed drastically beyond the allowed threshold value, the pixel is considered as the pixels of the object and the pixel values of the background model are updated based on this pixel value. The effectiveness of the proposed method is expressed with the value of recall and precision. The average recall value of the three videos is 62% and the value of its precision is 82%.Keywords--- Extraction Object, Background Modeling, Adaptive Modeling, underwater surveillance

Moving objects detection employing iterative update of the background

Detection of objects from a video is one of the basic issues in computer vision study. It is obvious that moving objects detection is particularly important, since they are those to which one should pay attention in walking, running, or driving a car. This paper proposes a method of detecting moving objects from a video as foreground objects by inferring backgrounds frame by frame. The proposed method can cope with various changes of a scene including large dynamical change of a scene in a video taken by a stationary/moving camera. Experimental results show satisfactory performance of the proposed method.The 21st International Symposium on Artificial Life and Robotics, January 20–22, 2016, Beppu, Oit

ビデオからの移動物体の検出に関する研究

九州工業大学博士学位論文 学位記番号:工博甲第381号 学位授与年月日:平成27年3月25日1 Introduction||2 Moving Object Detection Based on the Update of a Background Model||3 Moving Object Detection Employing a Moving Camera||4 Performance of the Method||5 Conclusion||In recent years, the video surveillance system for security and a driving safety system on the car have been growing rapidly. The video surveillance system has grown from a manual system to a fully autonomous system, whereas a driving safety system has evolved from a parking safety system to a collision avoidance system. The system requires a good ability to detect a moving object so as to be a reliable system. The problem that must be addressed in the detection of moving objects on a video is a dynamic background. In this thesis, we proposed a moving object detection method using sequential inference of the background to overcome the problem of the dynamic background. The sequential inference of the background uses a series of previous image frames to create a model of the background image for the current frame. After a background model is obtained, then the background subtraction can be done. The proposed method is applied to the video captured using a static camera and a moving camera. The detection of moving objects in a video captured by a moving camera is not as easy as the case using a static camera. Correspondence of pixels in the current image frame with the pixels of the previous image frame must be known in advance. The background model is formed using a bilinear interpolation of the previous image frame. The judgment of a pixel as the background or the foreground is done by subtracting the model of the background image from the current frame. An important stage in this method is updating normal distribution of the pixels on a background model. A background model is formed based on the value of the normal distribution which is updated with each frame of a video. The originality of this thesis is to propose novel ways of updating the normal distribution to obtain an effective background model. Experiments are performed on several videos. The results show that the proposed method can detect and extract moving objects that appear in a video scene successfully under various situations of the background. The effectiveness of the proposed method is recognized by recall, precision and F measure