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首页> 外文会议>Ground/air multisensor interoperability, integration, and networking for persistent ISR V >Robust Human Detection, Tracking, and Recognition in Crowded Urban Areas
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Robust Human Detection, Tracking, and Recognition in Crowded Urban Areas

机译：在拥挤的市区中进行可靠的人类检测，跟踪和识别

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In this paper, we present algorithms we recently developed to support an automated security surveillance system for very crowded urban areas. In our approach for human detection, the color features are obtained by taking the difference of R, G, B spectrum and converting R, G, B to HSV (Hue, Saturation, Value) space. Morphological patch filtering and regional minimum and maximum segmentation on the extracted features are applied for target detection, the human tracking process approach includes: 1) Color and intensity feature matching track candidate selection; 2) Separate three parallel trackers for color, bright (above mean intensity), and dim (below mean intensity) detections, respectively; 3) Adaptive track gate size selection for reducing false tracking probability; and 4) Forward position prediction based on previous moving speed and direction for continuing tracking even when detections are missed from frame to frame. The Human target recognition is improved with a Super-Resolution Image Enhancement (SRIE) process. This process can improve target resolution by 3-5 times and can simultaneously process many targets that are tracked. Our approach can project tracks from one camera to another camera with a different perspective viewing angle to obtain additional biometric features from different perspective angles, and to continue tracking the same person from the 2nd camera even though the person moved out of the Field of View (FOV) of the 1st camera with 'Tracking Relay'. Finally, the multiple cameras at different view poses have been geo-rectified to nadir view plane and geo-registered with Google-Earth (or other GIS) to obtain accurate positions (latitude, longitude, and altitude) of the tracked human for pin-point targeting and for a large area total human motion activity top-view. Preliminary tests of our algorithms indicate than high probability of detection can be achieved for both moving and stationary humans. Our algorithms can simultaneously track more than 100 human targets with averaged tracking period (time length) longer than the performance of the current state-of-the-art.
机译：在本文中，我们介绍了最近开发的算法，以支持非常拥挤的城市地区的自动安全监视系统。在我们的人体检测方法中，通过获取R，G，B光谱的差异并将R，G，B转换为HSV（色相，饱和度，值）空间来获得颜色特征。对提取出的特征进行形态斑块滤波和区域最小与最大分割用于目标检测，人工跟踪过程包括：1）颜色和强度特征匹配的候选跟踪选择； 2）分别使用三个并行的跟踪器分别检测颜色，明亮（平均强度以上）和暗淡（平均强度以下）； 3）自适应轨道门尺寸选择，以减少错误的跟踪概率； 4）基于先前的移动速度和方向的前向位置预测，即使在帧与帧之间都错过检测时也可以继续跟踪。通过超分辨率图像增强（SRIE）流程可以改善人类目标识别。此过程可以将目标分辨率提高3-5倍，并且可以同时处理跟踪的许多目标。我们的方法可以将轨迹从一台摄像机投射到具有不同视角的另一台摄像机，以从不同的视角获得其他生物特征，并继续从第二台摄像机跟踪同一个人，即使该人移出了视野（带有“跟踪中继”的第一台摄像机的FOV）。最后，将处于不同视角姿势的多台摄像机地理校正为最低点视线，并在Google地球（或其他GIS）中进行了地理注册，以获取被跟踪人员的精确位置（纬度，经度和海拔），以进行固定-点定位和大范围的总体人类运动活动俯视图。对我们算法的初步测试表明，无论是运动的还是静止的人，都可以实现很高的检测概率。我们的算法可以同时跟踪100多个人类目标，其平均跟踪时间（时间长度）比当前技术水平要长。

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