Detecting single-target changes in multiple object tracking: The case of peripheral vision

被引:0
|
作者
Christian Vater
Ralf Kredel
Ernst-Joachim Hossner
机构
[1] University of Bern,
来源
关键词
Covert attention; Perception; Motor control; Saccadic latency; Eyetracking; Sports;
D O I
暂无
中图分类号
学科分类号
摘要
In the present study, we investigated whether peripheral vision can be used to monitor multiple moving objects and to detect single-target changes. For this purpose, in Experiment 1, a modified multiple object tracking (MOT) setup with a large projection screen and a constant-position centroid phase had to be checked first. Classical findings regarding the use of a virtual centroid to track multiple objects and the dependency of tracking accuracy on target speed could be successfully replicated. Thereafter, the main experimental variations regarding the manipulation of to-be-detected target changes could be introduced in Experiment 2. In addition to a button press used for the detection task, gaze behavior was assessed using an integrated eyetracking system. The analysis of saccadic reaction times in relation to the motor response showed that peripheral vision is naturally used to detect motion and form changes in MOT, because saccades to the target often occurred after target-change offset. Furthermore, for changes of comparable task difficulties, motion changes are detected better by peripheral vision than are form changes. These findings indicate that the capabilities of the visual system (e.g., visual acuity) affect change detection rates and that covert-attention processes may be affected by vision-related aspects such as spatial uncertainty. Moreover, we argue that a centroid-MOT strategy might reduce saccade-related costs and that eyetracking seems to be generally valuable to test the predictions derived from theories of MOT. Finally, we propose implications for testing covert attention in applied settings.
引用
收藏
页码:1004 / 1019
页数:15
相关论文
共 50 条
  • [21] Transformer for multiple object tracking: Exploring locality to vision
    Wu, Shan
    Hadachi, Amnir
    Lu, Chaoru
    Vivet, Damien
    PATTERN RECOGNITION LETTERS, 2023, 170 : 70 - 76
  • [22] Pseudo-linear estimator for bearings-only single-target localization and tracking
    Xu, Zhigang
    Dong, Zhirong
    Dandao Xuebao/Journal of Ballistics, 2002, 14 (03):
  • [23] A single unexpected change in target-but not distractor motion impairs multiple object tracking
    Meyerhoff, Hauke S.
    Papenmeier, Frank
    Jahn, Georg
    Huff, Markus
    I-PERCEPTION, 2013, 4 (01): : 81 - 83
  • [24] A New Bayesian Edge-Linking Algorithm Using Single-Target Tracking Techniques
    Yoon, Ji Won
    SYMMETRY-BASEL, 2016, 8 (12):
  • [25] Considering Uncertain Parameters in Non-Gaussian Estimation for Single-Target and Multitarget Tracking
    McCabe, James S.
    DeMars, Kyle J.
    JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2017, 40 (09) : 2138 - 2150
  • [26] A novel adaptive deployment method for the single-target tracking of mobile wireless sensor networks
    Xiang, Shihu
    Yang, Jun
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 234
  • [27] Multiple Object Tracking: Case of Aircraft Detection and Tracking
    Ellouze, Ameni
    Ksantini, Mohamed
    Delmotte, Franois
    Karray, Mohamed
    2019 16TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2019, : 473 - 478
  • [28] Multiple-target tracking in human and machine vision
    Kamkar, Shiva
    Ghezloo, Fatemeh
    Moghaddam, Hamid Abrishami
    Borji, Ali
    Lashgari, Reza
    2020, Public Library of Science (16)
  • [29] Multiple-target tracking in human and machine vision
    Kamkar, Shiva
    Ghezloo, Fatemeh
    Moghaddam, Hamid Abrishami
    Borji, Ali
    Lashgari, Reza
    PLOS COMPUTATIONAL BIOLOGY, 2020, 16 (04)
  • [30] Target enhancement and distractor suppression in multiple object tracking
    Doran, Matthew M.
    Hoffman, James E.
    VISUAL COGNITION, 2010, 18 (01) : 126 - 129