Search results for: Kozo Tanigawa

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Tanigawa</title> <meta name="description" content="Search results for: Kozo Tanigawa"> <meta name="keywords" content="Kozo Tanigawa"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Kozo Tanigawa"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Kozo Tanigawa</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Determination of Neighbor Node in Consideration of the Imaging Range of Cameras in Automatic Human Tracking System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kozo%20Tanigawa">Kozo Tanigawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Tappei%20Yotsumoto"> Tappei Yotsumoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenichi%20Takahashi"> Kenichi Takahashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Kawamura"> Takao Kawamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazunori%20Sugahara"> Kazunori Sugahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An automatic human tracking system using mobile agent technology is realized because a mobile agent moves in accordance with a migration of a target person. In this paper, we propose a method for determining the neighbor node in consideration of the imaging range of cameras. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20tracking" title="human tracking">human tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=Pan%2FTilt%2FZoom" title=" Pan/Tilt/Zoom"> Pan/Tilt/Zoom</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20relation" title=" neighbor relation"> neighbor relation</a> </p> <a href="https://publications.waset.org/abstracts/11821/determination-of-neighbor-node-in-consideration-of-the-imaging-range-of-cameras-in-automatic-human-tracking-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Human Tracking across Heterogeneous Systems Based on Mobile Agent Technologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tappei%20Yotsumoto">Tappei Yotsumoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Atsushi%20Nomura"> Atsushi Nomura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kozo%20Tanigawa"> Kozo Tanigawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenichi%20Takahashi"> Kenichi Takahashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Kawamura"> Takao Kawamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazunori%20Sugahara"> Kazunori Sugahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a human tracking system, expanding a monitoring range of one system is complicating the management of devices and increasing its cost. Therefore, we propose a method to realize a wide-range human tracking by connecting small systems. In this paper, we examined an agent deploy method and information contents across the heterogeneous human tracking systems. By implementing the proposed method, we can construct a human tracking system across heterogeneous systems, and the system can track a target continuously between systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20tracking%20system" title="human tracking system">human tracking system</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20systems" title=" heterogeneous systems"> heterogeneous systems</a> </p> <a href="https://publications.waset.org/abstracts/11702/human-tracking-across-heterogeneous-systems-based-on-mobile-agent-technologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11702.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">536</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Event Data Representation Based on Time Stamp for Pedestrian Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuta%20Nakano">Yuta Nakano</a>, <a href="https://publications.waset.org/abstracts/search?q=Kozo%20Kajiwara"> Kozo Kajiwara</a>, <a href="https://publications.waset.org/abstracts/search?q=Atsushi%20Hori"> Atsushi Hori</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeshi%20Fujita"> Takeshi Fujita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=event%20camera" title="event camera">event camera</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20vision%20sensor" title=" dynamic vision sensor"> dynamic vision sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20representation" title=" data representation"> data representation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20energy%20consumption" title=" low energy consumption"> low energy consumption</a> </p> <a href="https://publications.waset.org/abstracts/164424/event-data-representation-based-on-time-stamp-for-pedestrian-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164424.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div 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Search results for: Kozo Tanigawa