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Adaptive online camera coordination for multi-camera multi-target surveillance

机译:自适应在线摄像机协调,实现多摄像机多目标监控

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Online camera selection is introduced as a result of the improved mobility of cameras and the increased scale of surveillance systems. Most existing camera assignment algorithms achieve an optimal observation under the assumption of the unlimited camera computational capacities. However, practical surveillance systems experience resource limitation and see a degradation in the system performance as the number of objects to be processed increases. To address this issue, we propose an adaptive camera assignment algorithm considering the limited camera computational capacities. In so doing, camera resources can be dynamically allocated to multiple objects according to their priorities and the current camera computational load. Experimental results illustrate that the proposed camera assignment algorithm is capable of maintaining a constant frame rate and achieving a substantially decreased object rejection rate in comparison with the algorithm presented by Bakhtari and Benhabib.
机译:由于提高了摄像机的移动性和监视系统的规模,引入了在线摄像机选择。大多数现有的摄像机分配算法都是在无限制的摄像机计算能力的前提下实现最佳观察的。但是,实际的监视系统会遇到资源限制,并且随着要处理的对象数量的增加,系统性能会下降。为了解决这个问题,我们提出了一种考虑到有限的相机计算能力的自适应相机分配算法。这样,摄像机资源可以根据它们的优先级和当前摄像机的计算负荷动态分配给多个对象。实验结果表明,与Bakhtari和Benhabib提出的算法相比,所提出的相机分配算法能够保持恒定的帧频并显着降低物体拒绝率。

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