What is a visual object? Evidence from target merging in multiple object tracking

被引:210
|
作者
Scholl, BJ
Pylyshyn, ZW
Feldman, J
机构
[1] Harvard Univ, Cambridge, MA 02138 USA
[2] Rutgers State Univ, New Brunswick, NJ 08903 USA
关键词
visual object; target merging; multiple object tracking;
D O I
10.1016/S0010-0277(00)00157-8
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
The notion that visual attention can operate over visual objects in addition to spatial locations has recently received much empirical support, but there has been relatively little empirical consideration of what can count as an 'object' in the first place. We have investigated this question in the context of the multiple object tracking paradigm, in which subjects must track a number of independently and unpredictably moving identical items in a field of identical distracters. What types of feature clusters can be tracked in this manner? In other words, what counts as an 'object' in this task? We investigated this question with a technique we call target merging: we alter tracking displays so that distinct target and distracter locations appear perceptually to be parts of the same object by merging pairs of items (one target with one distracter) in various ways - for example, by connecting item locations with a simple line segment, by drawing the convex hull of the two items. and so forth. The data show that target merging makes the tracking task far more difficult to varying degrees depending on exactly how the items are merged. The effect is perceptually salient, involving in some conditions a total destruction of subjects' capacity to track multiple items. These studies provide strong evidence for the object-based nature of tracking, confirming that in some contexts attention must be allocated to objects rather than arbitrary collections of features. In addition, the results begin to reveal the types of spatially organized scene components that can be independently attended as a function of properties such as connectedness, part structure, and other types of perceptual grouping. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:159 / 177
页数:19
相关论文
共 50 条
  • [31] Visual object tracking: A survey
    Chen, Fei
    Wang, Xiaodong
    Zhao, Yunxiang
    Lv, Shaohe
    Niu, Xin
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2022, 222
  • [32] Visual tracking and object recognition
    Tannenbaum, A
    Yezzi, A
    Goldstein, A
    NONLINEAR CONTROL SYSTEMS 2001, VOLS 1-3, 2002, : 1539 - 1542
  • [33] Velocity cues improve visual search and multiple object tracking
    Fencsik, David E.
    Urrea, Jessenia
    Place, Skyler S.
    Wolfe, Jeremy M.
    Horowitz, Todd S.
    VISUAL COGNITION, 2006, 14 (01) : 92 - 95
  • [34] The impact of auditory, visual and crossmodal cues on multiple object tracking
    Focker, Julia
    Atkins, Polly
    Vantzos, Foivos-Christos
    Wilhelm, Maximilian
    Schenk, Thomas
    Meyerhoff, Hauke S.
    I-PERCEPTION, 2021, 12 (04): : 4 - 4
  • [35] Visual multiple-object tracking for unknown clutter rate
    Kim, Du Yong
    IET COMPUTER VISION, 2018, 12 (05) : 728 - 734
  • [36] Multiple Context Features in Siamese Networks for Visual Object Tracking
    Morimitsu, Henrique
    COMPUTER VISION - ECCV 2018 WORKSHOPS, PT I, 2019, 11129 : 116 - 131
  • [37] Novel color-based target representation for visual object tracking
    Gu, Yuantao
    Chen, Yilun
    Wang, Jie
    2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13, 2006, : 1449 - 1452
  • [38] Learning Geometry Information of Target for Visual Object Tracking with Siamese Networks
    Chen, Hang
    Zhang, Weiguo
    Yan, Danghui
    SENSORS, 2021, 21 (23)
  • [39] Target-Cognisant Siamese Network for Robust Visual Object Tracking
    Jiang, Yingjie
    Song, Xiaoning
    Xu, Tianyang
    Feng, Zhenhua
    Wu, Xiaojun
    Kittler, Josef
    Pattern Recognition Letters, 2022, 163 : 129 - 135
  • [40] Target-Cognisant Siamese Network for Robust Visual Object Tracking *
    Jiang, Yingjie
    Song, Xiaoning
    Xu, Tianyang
    Feng, Zhenhua
    Wu, Xiaojun
    Kittler, Josef
    PATTERN RECOGNITION LETTERS, 2022, 163 : 129 - 135