GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
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DOI：
10.1007/978-3-642-33709-3_25
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发表时间：
2012-10
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影响因子：
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通讯作者：
Amir Zamir;Afshin Dehghan;M. Shah
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文献类型：
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作者：
Amir Zamir;Afshin Dehghan;M. Shah
数据关联是任何人类跟踪系统的重要组成部分。目前的大多数方法，如二部匹配法，都将序列的有限时间局部性融入到数据关联问题中，这使得它们固有地容易发生ID切换，并因长期遮挡、背景杂乱和场景拥挤而造成困难。与有限时间局部性方法将几个帧合并到数据关联问题中不同，我们结合整个时间跨度，一次解决一个对象的数据关联问题，同时隐式地合并其余对象。为了达到这一目的，我们利用广义最小团图来解决我们的数据关联方法的优化问题。我们提出的方法产生了一种更好的数据关联方法，这一点得到了我们优越的结果的支持。实验表明，与现有的跟踪方法相比，该方法在跟踪城市中心[1]、TUD交叉[2]、TUD-Stadtmitte[2]、PETS2009[3]以及停车场等不同序列上都有明显的改进。
Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes.We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of Town Center [1], TUD-crossing [2], TUD-Stadtmitte [2], PETS2009 [3], and a new sequence called Parking Lot compared to the state of the art methods.