In everyday life, we often view objects through a limited aperture (e.g., soccer players on TV or cars slipping into our blind spot on a busy road), where objects often move out of view and reappear in a different place later. We modelled this situation in a series of multiple object tracking (MOT) experiments, in which we introduced a cover on the edges of the observed area and manipulated its width. This method introduced systematic occlusions, which were longer than those used in previous MOT studies. Experiment 1 (N = 50) showed that tracking under such conditions is possible, although difficult. An item-level analysis confirmed that people made more errors in targets that were covered longer and more often. In Experiment 2 (N = 50), we manipulated the tracking workload and found that the participants were less affected by the cover when the tracking load was low. In Experiment 3 (N = 50), we asked the participants to keep track of the objects' identities (multiple identity tracking [MIT]). Although MIT is subjectively more demanding, memorising identities improved performance in the most difficult cover conditions. Contrary to previous reports, we also found that even partial occlusions negatively affected tracking.
- Keywords
- Visual attention, multiple identity tracking, multiple object tracking, occlusion, visual memory,
- MeSH
- Humans MeSH
- Attention * MeSH
- Motion Perception * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
In dynamic environments, a central task of the attentional system is to keep track of objects changing their spatial location over time. In some instances, it is sufficient to track only the spatial locations of moving objects (i.e., multiple object tracking; MOT). In other instances, however, it is also important to maintain distinct identities of moving objects (i.e., multiple identity tracking; MIT). Despite previous research, it is not clear whether MOT and MIT performance emerge from the same tracking mechanism. In the present report, we study gaze coherence (i.e., the extent to which participants repeat their gaze behaviour when tracking the same object locations twice) across repeated MOT and MIT trials. We observed more substantial gaze coherence in repeated MOT trials compared to the repeated MIT trials or mixed MOT-MIT trial pairs. A subsequent simulation study suggests that MOT is based more on a grouping mechanism than MIT, whereas MIT is based more on a target-jumping mechanism than MOT. It thus appears unlikely that MOT and MIT emerge from the same basic tracking mechanism.
- Keywords
- Attention, Eye movements, Multiple identitty, Multiple object tracking, Tracking,
- MeSH
- Correlation of Data MeSH
- Humans MeSH
- Young Adult MeSH
- Models, Neurological * MeSH
- Fixation, Ocular * physiology MeSH
- Motion MeSH
- Probability MeSH
- Reproducibility of Results MeSH
- Pattern Recognition, Visual * physiology MeSH
- Photic Stimulation MeSH
- Motion Perception * physiology MeSH
- Check Tag
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Contrary to other tasks (free viewing, recognition, visual search), participants often fail to recognize repetition of trials in multiple object tracking (MOT). This study examines the intra- and interindividual variability of eye movements in repeated MOT trials along with the adherence of eye movements to the previously described strategies. I collected eye movement data from 20 subjects during 64 MOT trials at slow speed (5°/s). Half of the trials were repeated four times, and the remaining trials were unique. I measured the variability of eye-movement patterns during repeated trials using normalized scanpath saliency extended to the temporal domain. People tended to make similar eye movements during repeated presentations (with no or vague feeling of repetition) and the interindividual similarity remained at the same level over time. Several strategies (centroid strategy and its variants) were compared with data and they accounted for 48.8% to 54.3% of eye-movement variability, which was less then variability explained by other peoples' eye movements (68.6%). The results show that the observed intra- and interindividual similarity of eye movements is only partly explained by the current models.
- Keywords
- active vision, attention, eye movements, multiple object tracking, spatial vision,
- MeSH
- Humans MeSH
- Young Adult MeSH
- Saccades physiology MeSH
- Photic Stimulation MeSH
- Space Perception physiology MeSH
- Form Perception physiology MeSH
- Check Tag
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Multiple object tracking (MOT) and multiple identity tracking (MIT) each measure the ability to track moving objects visually. While prior investigators have mainly compared athletes and non-athletes on MOT, MIT more closely resembles dynamic real-life environments. Here we compared the performance and gaze behavior of handball players with non-athletes on both MOT and MIT. Since previous researchers have shown that MOT and MIT engage different eye movement strategies, we had participants track 3-5 targets among 10 moving objects. In MOT, the objects were identical, while in MIT they differed in shape and color. Although we observed no group differences for tracking accuracy, the eye movements of athletes were more target-oriented than those of non-athletes. We concluded that tasks and stimuli intended by researchers to demonstrate that athletes' show better object tracking than non-athletes should be specific to the athletes' type of sport and should use more perception-action coupled measures. An implication of this conclusion is that the differences in object tracking skills between athletes and non-athletes is highly specific to the skills demanded by the athletes' sport.
- Keywords
- athletes, eye tracking, gaze behavior, multiple identity tracking, multiple object tracking, non-athletes,
- MeSH
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Fixation, Ocular physiology MeSH
- Psychomotor Performance physiology MeSH
- Athletes * psychology MeSH
- Athletic Performance physiology MeSH
- Sports * psychology physiology MeSH
- Motion Perception * physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Although the Multiple Object Tracking (MOT) task is a widely used experimental method for studying divided attention, tracking objects in the real world usually looks different. For example, in the real world, objects are usually clearly distinguishable from each other and also possess different movement patterns. One such case is tracking groups of creatures, such as tracking fish in an aquarium. We used movies of fish in an aquarium and measured general tracking performance in this task (Experiment 1). In Experiment 2, we compared tracking accuracy within-subjects in fish tracking, tracking typical MOT stimuli, and in a third condition using standard MOT uniform objects which possessed movement patterns similar to the real fish. This third condition was added to further examine the impact of different motion characteristics on tracking performance. Results within a Bayesian framework showed that tracking real fish shares similarities with tracking simple objects in a typical laboratory MOT task. Furthermore, we observed a close relationship between performance in both laboratory MOT tasks (typical and fish-like) and real fish tracking, suggesting that the commonly used laboratory MOT task possesses a good level of ecological validity.
- Keywords
- Attention, Ecological validity, Fish, Modelling, Multiple object tracking,
- MeSH
- Bayes Theorem MeSH
- Humans MeSH
- Attention MeSH
- Motion Perception * MeSH
- Research Design MeSH
- Rubella * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
In everyday life, people often need to track moving objects. Recently, a topic of discussion has been whether people rely solely on the locations of tracked objects, or take their directions into account in multiple object tracking (MOT). In the current paper, we pose a related question: do people utilise extrapolation in their gaze behaviour, or, in more practical terms, should the mathematical models of gaze behaviour in an MOT task be based on objects' current, past or anticipated positions? We used a data-driven approach with no a priori assumption about the underlying gaze model. We repeatedly presented the same MOT trials forward and backward and collected gaze data. After reversing the data from the backward trials, we gradually tested different time adjustments to find the local maximum of similarity. In a series of four experiments, we showed that the gaze position lagged by approximately 110 ms behind the scene content. We observed the lag in all subjects (Experiment 1). We further experimented to determine whether tracking workload or predictability of movements affect the size of the lag. Low workload led only to a small non-significant shortening of the lag (Experiment 2). Impairing the predictability of objects' trajectories increased the lag (Experiments 3a and 3b). We tested our observations with predictions of a centroid model: we observed a better fit for a model based on the locations of objects 110 ms earlier. We conclude that mathematical models of gaze behaviour in MOT should account for the lags.
- Keywords
- Eye movements and visual attention, Motion: Integration,
- MeSH
- Time Factors MeSH
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Fixation, Ocular physiology MeSH
- Attention physiology MeSH
- Models, Psychological * MeSH
- Pattern Recognition, Visual physiology MeSH
- Motion Perception physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
A PC-based system with TV input for automatic tracking of a single and contrast object in 2D in a homogeneous and stationary environment has been developed and applied to Morris water maze experiments. Further development of the system aimed at broader support of experiments, reduction of requirements on the stationarity and homogeneity of the scene background and on multiple-object tracking is discussed. The computer control of active light markers of the tracked object applicable to multiple-objects tracking in a time-sharing regime is also mentioned in the conclusion. The latter extension of the system can be applied to kinematic studies in biomechanics, sport and rehabilitation medicine.
We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.
- MeSH
- Cell Nucleus chemistry MeSH
- Cell Tracking methods MeSH
- Microscopy, Fluorescence methods MeSH
- Rats MeSH
- Humans MeSH
- Mesenchymal Stem Cells cytology MeSH
- Cell Line, Tumor MeSH
- Image Processing, Computer-Assisted methods MeSH
- Cell Shape physiology MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND AND OBJECTIVE: The geodesic ray-tracing method has shown its effectiveness for the reconstruction of fibers in white matter structure. Based on reasonable metrics on the spaces of the diffusion tensors, it can provide multiple solutions and get robust to noise and curvatures of fibers. The choice of the metric on the spaces of diffusion tensors has a significant impact on the outcome of this method. Our objective is to suggest metrics and modifications of the algorithms leading to more satisfactory results in the construction of white matter tracts as geodesics. METHODS: Starting with the DTI modality, we propose to rescale the initially chosen metric on the space of diffusion tensors to increase the geodetic cost in the isotropic regions. This change should be conformal in order to preserve the angles between crossing fibers. We also suggest to enhance the methods to be more robust to noise and to employ the fourth order tensor data in order to handle the fiber crossings properly. RESULTS: We propose a way to choose the appropriate conformal class of metrics where the metric gets scaled according to tensor anisotropy. We use the logistic functions, which are commonly used in statistics as cumulative distribution functions. To prevent deviation of geodesics from the actual paths, we propose a hybrid ray-tracing approach. Furthermore, we suggest how to employ diagonal projections of 4th order tensors to perform fiber tracking in crossing regions. CONCLUSIONS: The algorithms based on the newly suggested methods were succesfuly implemented, their performance was tested on both synthetic and real data, and compared to some of the previously known approaches.
We propose a learning approach to tracking explicitly minimizing the computational complexity of the tracking process subject to user-defined probability of failure (loss-of-lock) and precision. The tracker is formed by a Number of Sequences of Learned Linear Predictors (NoSLLiP). Robustness of NoSLLiP is achieved by modeling the object as a collection of local motion predictors--object motion is estimated by the outlier-tolerant RANSAC algorithm from local predictions. Efficiency of the NoSLLiP tracker stems from (i) the simplicity of the local predictors and (ii) from the fact that all design decisions--the number of local predictors used by the tracker, their computational complexity (i.e. the number of observations the prediction is based on), locations as well as the number of RANSAC iterations are all subject to the optimization (learning) process. All time-consuming operations are performed during the learning stage--tracking is reduced to only a few hundreds integer multiplications in each step. On PC with 1xK8 3200+, a predictor evaluation requires about 30 microseconds. The proposed approach is verified on publicly-available sequences with approximately 12000 frames with ground-truth. Experiments demonstrates, superiority in frame rates and robustness with respect to the SIFT detector, Lucas-Kanade tracker and other trackers.
- Publication type
- Journal Article MeSH
