Bayesian Device-Free Localization and Tracking in A Binary RF Sensor Network > 자유게시판

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Received-sign-power-based (RSS-primarily based) iTagPro device-free localization (DFL) is a promising technique because it is able to localize the individual with out attaching any electronic system. This technology requires measuring the RSS of all hyperlinks within the community constituted by several radio frequency (RF) sensors. It is an power-intensive task, especially when the RF sensors work in traditional work mode, through which the sensors straight ship raw RSS measurements of all links to a base station (BS). The standard work mode is unfavorable for the facility constrained RF sensors as a result of the amount of data delivery will increase dramatically because the variety of sensors grows. In this paper, we propose a binary work mode through which RF sensors ship the hyperlink states instead of uncooked RSS measurements to the BS, which remarkably reduces the quantity of information delivery. Moreover, we develop two localization strategies for the binary work mode which corresponds to stationary and transferring target, respectively. The first localization technique is formulated based mostly on grid-based most probability (GML), which is able to attain global optimum with low on-line computational complexity. The second localization technique, nevertheless, uses particle filter (PF) to trace the goal when fixed snapshots of link stats are available. Real experiments in two different kinds of environments have been carried out to evaluate the proposed methods. Experimental results present that the localization and monitoring performance underneath the binary work mode is comparable to the these in conventional work mode while the power effectivity improves considerably.

Object detection is broadly used in robot navigation, clever video surveillance, industrial inspection, aerospace and many different fields. It is a crucial branch of picture processing and iTagPro device computer imaginative and prescient disciplines, and is also the core part of clever surveillance techniques. At the identical time, target detection can be a primary algorithm in the sector of pan-identification, which performs a vital role in subsequent duties reminiscent of face recognition, gait recognition, crowd counting, and occasion segmentation. After the primary detection module performs goal detection processing on the video frame to acquire the N detection targets in the video frame and the primary coordinate information of every detection target, the above method It also contains: displaying the above N detection targets on a display screen. The primary coordinate information corresponding to the i-th detection goal; obtaining the above-mentioned video frame; positioning within the above-talked about video body according to the first coordinate info corresponding to the above-mentioned i-th detection target, acquiring a partial image of the above-mentioned video body, and determining the above-mentioned partial image is the i-th picture above.

The expanded first coordinate data corresponding to the i-th detection target; the above-mentioned first coordinate data corresponding to the i-th detection goal is used for positioning within the above-mentioned video frame, together with: according to the expanded first coordinate information corresponding to the i-th detection goal The coordinate information locates within the above video body. Performing object detection processing, if the i-th picture includes the i-th detection object, buying position information of the i-th detection object within the i-th picture to obtain the second coordinate information. The second detection module performs target detection processing on the jth picture to find out the second coordinate data of the jth detected goal, the place j is a positive integer not higher than N and ItagPro not equal to i. Target detection processing, obtaining a number of faces in the above video body, and first coordinate information of each face; randomly acquiring goal faces from the above multiple faces, and intercepting partial photographs of the above video frame in keeping with the above first coordinate data ; performing target detection processing on the partial picture by means of the second detection module to acquire second coordinate info of the target face; displaying the target face in accordance with the second coordinate data.

Display multiple faces within the above video frame on the screen. Determine the coordinate list in accordance with the primary coordinate information of every face above. The first coordinate information corresponding to the goal face; buying the video body; and positioning within the video frame in keeping with the primary coordinate data corresponding to the target face to acquire a partial image of the video body. The prolonged first coordinate information corresponding to the face; the above-talked about first coordinate information corresponding to the above-mentioned target face is used for positioning within the above-talked about video frame, including: based on the above-mentioned prolonged first coordinate info corresponding to the above-talked about target face. Within the detection course of, if the partial image includes the goal face, buying place info of the target face in the partial image to acquire the second coordinate data. The second detection module performs goal detection processing on the partial image to find out the second coordinate data of the opposite goal face.

In: performing goal detection processing on the video body of the above-talked about video by the above-mentioned first detection module, acquiring a number of human faces in the above-mentioned video frame, and the first coordinate info of every human face; the local picture acquisition module is used to: from the above-talked about a number of The goal face is randomly obtained from the private face, and the partial image of the above-mentioned video body is intercepted according to the above-talked about first coordinate information; the second detection module is used to: iTagPro device carry out target detection processing on the above-mentioned partial image by means of the above-talked about second detection module, in order to acquire the above-mentioned The second coordinate information of the target face; a show module, configured to: display the goal face in keeping with the second coordinate data. The goal monitoring methodology described in the first aspect above might understand the target choice technique described in the second side when executed.