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基于特征的自动分组在多目标跟踪中的应用。

Automatic feature-based grouping during multiple object tracking.

机构信息

Department of Psychology, University of California, Los Angeles.

Rutgers University Center for Cognitive Science, Rutgers University.

出版信息

J Exp Psychol Hum Percept Perform. 2013 Dec;39(6):1625-1637. doi: 10.1037/a0031750. Epub 2013 Mar 4.

Abstract

Contour interpolation automatically binds targets with distractors to impair multiple object tracking (Keane, Mettler, Tsoi, & Kellman, 2011). Is interpolation special in this regard or can other features produce the same effect? To address this question, we examined the influence of eight features on tracking: color, contrast polarity, orientation, size, shape, depth, interpolation, and a combination (shape, color, size). In each case, subjects tracked 4 of 8 objects that began as undifferentiated shapes, changed features as motion began (to enable grouping), and returned to their undifferentiated states before halting. We found that intertarget grouping improved performance for all feature types except orientation and interpolation (Experiment 1 and Experiment 2). Most importantly, target-distractor grouping impaired performance for color, size, shape, combination, and interpolation. The impairments were, at times, large (>15% decrement in accuracy) and occurred relative to a homogeneous condition in which all objects had the same features at each moment of a trial (Experiment 2), and relative to a "diversity" condition in which targets and distractors had different features at each moment (Experiment 3). We conclude that feature-based grouping occurs for a variety of features besides interpolation, even when irrelevant to task instructions and contrary to the task demands, suggesting that interpolation is not unique in promoting automatic grouping in tracking tasks. Our results also imply that various kinds of features are encoded automatically and in parallel during tracking.

摘要

轮廓插值自动将目标与干扰项绑定,从而损害多目标跟踪(Keane、Mettler、Tsoi 和 Kellman,2011)。在这方面插值是否特殊,或者其他特征是否可以产生相同的效果?为了解决这个问题,我们研究了八个特征对跟踪的影响:颜色、对比度极性、方向、大小、形状、深度、插值和组合(形状、颜色、大小)。在每种情况下,受试者跟踪 8 个目标中的 4 个,这些目标最初是未分化的形状,随着运动的开始而改变特征(以实现分组),并在停止前恢复到未分化状态。我们发现,除了方向和插值之外,所有特征类型的目标间分组都可以提高性能(实验 1 和实验 2)。最重要的是,颜色、大小、形状、组合和插值的目标-干扰分组会损害性能。这些损害有时很大(准确性降低超过 15%),并且相对于在试验的每个时刻所有物体都具有相同特征的同质条件(实验 2),以及相对于在每个时刻目标和干扰具有不同特征的“多样性”条件(实验 3)。我们得出的结论是,除了插值之外,各种特征都可以进行基于特征的分组,即使这与任务指令无关且与任务要求相反,这表明插值在促进跟踪任务中的自动分组方面并不是独一无二的。我们的结果还表明,在跟踪过程中,各种特征会自动且并行地被编码。

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