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首页> 外文会议>Conference on Real-Time Image Processing; 20080128-29; San Jose,CA(US) >Improved Tracking by Decoupling Camera and Target Motion
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Improved Tracking by Decoupling Camera and Target Motion

机译：通过解耦相机和目标运动改善跟踪

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摘要

Video tracking is widely used for surveillance, security, and defense purposes. In cases where the camera is not fixed due to pans and tilts, or due to being fixed on a moving platform, tracking can become more difficult. Camera motion must be taken into account, and objects that come and go from the field of view should be continuously and uniquely tracked. We propose a tracking system that can meet these needs by using a frame registration technique to estimate camera motion. This estimate is then used as the input control signal to a Kalman filter which estimates the target's motion model based on measurements from a mean-shift localization scheme. Thus we decouple the camera and object motion and recast the problem in terms of a principled control theory solution. Our experiments show that using a controller built on these principles we are able to track videos with multiple objects in sequences with moving cameras. Furthermore, the techniques are computationally efficient and allow us to accomplish these results in real-time. Of specific importance is that when objects are lost off-frame they can still be uniquely identified and reacquired when they return to the field of view.
机译：视频跟踪广泛用于监视，安全和防御目的。如果由于摇摄和倾斜或由于固定在移动平台上而导致相机无法固定，则跟踪会变得更加困难。必须考虑摄像机的运动，并且应该连续唯一地跟踪从视场进出的物体。我们提出了一种跟踪系统，该系统可以通过使用帧配准技术来估计相机运动来满足这些需求。然后，该估计值用作卡尔曼滤波器的输入控制信号，该滤波器基于来自均值漂移定位方案的测量值来估计目标的运动模型。因此，我们将摄像机和物体的运动解耦，并根据有原则的控制理论解决方案重现该问题。我们的实验表明，使用基于这些原理的控制器，我们可以使用移动摄像机按顺序跟踪具有多个对象的视频。此外，该技术在计算上是有效的，并允许我们实时完成这些结果。特别重要的是，当物体失格丢失时，当它们返回视野时仍然可以唯一地识别和重新获取它们。

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