scholarly journals Gamma coherence mediates interhemispheric integration during multiple object tracking

2020 ◽  
Vol 123 (5) ◽  
pp. 1630-1644
Author(s):  
Nicholas S. Bland ◽  
Jason B. Mattingley ◽  
Martin V. Sale
Keyword(s):  
Individual Differences ◽  
Object Tracking ◽  
Objective Measure ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Eeg Coherence ◽  
Multiple Object ◽  
Predicting Performance ◽  
Trial Basis ◽  
Gamma Coherence

Using a multiple object tracking paradigm, we were able to manipulate the need for interhemispheric integration on a per-trial basis, while also having an objective measure of integration efficacy (i.e., tracking performance). We show that tracking performance reflects a cost of integration, which correlates with individual differences in interhemispheric EEG coherence. Gamma coherence appears to uniquely benefit between-hemifield tracking, predicting performance both across participants and across trials.

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

scholarly journals MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

2020 ◽  
Author(s):  
Patrick Dendorfer ◽  
Aljos̆a Os̆ep ◽  
Anton Milan ◽  
Konrad Schindler ◽  
Daniel Cremers ◽  
...  
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Objective Measure ◽  
Multiple Object Tracking ◽  
Future Research ◽  
Single Camera ◽  
Multiple Object ◽  
Future Research Directions ◽  
Self Driving Cars ◽  
Measure Of Performance

AbstractStandardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.

scholarly journals Action Video Game Experience Does Not Predict Multiple Object Tracking Performance

2014 ◽  
Vol 14 (10) ◽  
pp. 353-353 ◽  
Author(s):  
C. Stothart ◽  
W. Boot ◽  
D. Simons ◽  
A. Beyko
Keyword(s):  
Object Tracking ◽  
Video Game ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Action Video Game ◽  
Multiple Object

scholarly journals Individual Differences in Visual Attention: A Short, Reliable, Open Source, and Multilingual Test of Multiple Object Tracking in PsychoPy

2020 ◽  
Author(s):  
Hauke S. Meyerhoff ◽  
Frank Papenmeier
Keyword(s):  
Individual Differences ◽  
Visual Attention ◽  
Object Tracking ◽  
Open Source ◽  
Multiple Object Tracking ◽  
Object Motion ◽  
Laboratory Research ◽  
Multiple Object ◽  
Psychometric Measures ◽  
Interesting Approach

Individual differences in attentional abilities provide an interesting approach in studying visual attention as well as the relation of attention to other psychometric measures. However, recent research has demonstrated that many tasks from experimental research are not suitable for individual differences research as they fail to capture these differences reliably. Here, we provide a test for individual differences in visual attention which relies on the multiple object tracking task (MOT). This test captures individual differences reliably in 6-15 minutes. Within the task the participants have to maintain a set of targets (among identical distractors) across an interval of object motion. It captures the efficiency of attentional deployment. Importantly, this test was explicitly designed and tested for reliability under conditions that match those of most laboratory research (restricted sample of students, approximately n = 50). The test is free to use and runs fully under open source software. In order to facilitate the application of the test, we have translated it into 16 common languages (Chinese, Danish, Dutch, English, Finnish, French, German, Italian, Japanese, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Turkish). The test can be downloaded at https://osf.io/qy6nb/. We hope that this MOT test supports researchers whose field of study requires capturing individual differences in visual attention reliably.

scholarly journals Simulating multiple object tracking performance using a Kalman filter model

2015 ◽  
Vol 15 (12) ◽  
pp. 465
Author(s):  
Gregory Zelinsky ◽  
Ashley Sherman ◽  
Tomás Yago
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Filter Model ◽  
Multiple Object

Distractor Locations Influence Multiple Object Tracking Beyond Interobject Spacing

2015 ◽  
Vol 62 (3) ◽  
pp. 170-180 ◽  
Author(s):  
Hauke S. Meyerhoff ◽  
Frank Papenmeier ◽  
Georg Jahn ◽  
Markus Huff
Keyword(s):  
Object Tracking ◽  
Target Tracking ◽  
Moving Objects ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Displacement Effect ◽  
Inhibitory Processes ◽  
Multiple Object ◽  
Displacement Angle

Human observers are able to keep track of several independently moving objects among other objects. Within theories of multiple object tracking (MOT), distractors are assumed to influence tracking performance only by their distance toward the next target. In order to test this assumption, we designed a variant of the MOT paradigm that involved spatially arranged target-distractor pairs and sudden displacements of distractors during a brief flash. Critically, these displacements maintained target-distractor spacing. Our results show that displacing distractors hurts tracking performance (Experiment 1). Importantly, target-distractor confusions occur within target-distractor pairs with displaced distractors (Experiment 2). This displacement effect increases with an increasing displacement angle (Experiment 3) but is equal at different distances between target and distractor (Experiment 4). This finding illustrates that distractors influence tracking performance beyond pure interobject spacing. We discuss how inhibitory processes as well as relations between targets and distractors might interfere with target tracking.

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