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首页> 外文期刊>International Journal of Innovative Computing Information and Control >AN EFFICIENT MULTIPLE CUES SYNTHESIS FOR HUMAN TRACKING USING A PARTICLE FILTERING FRAMEWORK
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AN EFFICIENT MULTIPLE CUES SYNTHESIS FOR HUMAN TRACKING USING A PARTICLE FILTERING FRAMEWORK

机译：使用粒子滤波框架进行人跟踪的高效多线索合成

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摘要

In visual tracking situations, the appearance of both humans and the surrounding scenes may experience enormous variations due to changes in the scale and viewing angles, partial occlusions or in the interactions of a crowd. These challenges may weaken the effectiveness of a dedicated target observation model, even when based on multiple cues. Towards this end, we propose a new way to integrate a multiple cues synthesis for effective human tracking in video sequences by using particle filtering to process the features from video frames. We adapt the method used to combine specifically devised models based on different cues in this tracker in order to enhance the discriminative power of the integrated observation model in its local neighborhood and to minimize the occlusion problem. This is achieved by an efficient observation model that is formulated from multiple visual cues, namely the color and the edge shape, which are described using highly non-linear models. There is difficulty in representing the human spatial shape in a cluttered background; this is the main barrier in constructing an efficient observation model. This difficulty can be minimized by representing the human body using a Multi-Part Histogram (MPH) combined with a Distance Transform (DT) image. The reference and target objects are represented by a sub-region using integral image techniques. Each region has its own histogram; we calculate the weight of each particle based on its regional position in the target object. The most weighted particle settles on the central position of the bounded target and gradually decreases the particle weight vertically and horizontally from this central position. The advantages of this are an increased robustness and an improved accuracy against false target tracking and severe occlusions. An extensive evaluation of the proposed algorithm was investigated and compared to another color based human tracker using the CAVIAR and the perceptiVU databases, as well as our own.
机译：在视觉跟踪情况下，由于比例和视角的变化，部分遮挡或人群的互动，人和周围场景的外观都可能会发生巨大变化。这些挑战可能会削弱专用目标观测模型的有效性，即使基于多个线索也是如此。为此，我们提出了一种新的方法，可以通过使用粒子滤波处理视频帧中的特征来集成多线索合成，从而有效地在视频序列中进行人类跟踪。我们调整了用于在此跟踪器中基于不同线索组合专门设计的模型的方法，以增强集成观测模型在其局部邻域中的判别能力并最大程度地减少遮挡问题。这是通过有效的观察模型实现的，该模型由多个视觉提示（即颜色和边缘形状）构成，这些视觉提示使用高度非线性的模型进行描述。在凌乱的背景下很难表现出人类的空间形状；这是构建有效观测模型的主要障碍。通过使用多部分直方图（MPH）与距离变换（DT）图像相结合来代表人体，可以最大程度地降低此难度。参考对象和目标对象由使用集成图像技术的子区域表示。每个区域都有自己的直方图；我们根据每个粒子在目标对象中的区域位置来计算其重量。权重最大的粒子位于边界目标的中心位置，并从该中心位置垂直和水平方向逐渐减小粒子的重量。这样做的优点是提高了鲁棒性，并提高了针对错误目标跟踪和严重遮挡的准确性。研究了对所提出算法的广泛评估，并将其与使用CAVIAR和perceptiVU数据库以及我们自己的基于颜色的另一种基于人类的追踪器进行了比较。

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