CAT: Centerness-Aware Anchor-Free Tracker

Accurate and robust scale estimation in visual object tracking is a challenging task. To obtain a scale estimation of the target object, most methods rely either on a multi-scale searching scheme or on refining a set of predefined anchor boxes. These methods require heuristically selected parameters, such as scale factors of the multi-scale searching scheme, or sizes and aspect ratios of the predefined candidate anchor boxes. On the contrary, a centerness-aware anchor-free tracker (CAT) is designed in this work. First, the location and scale of the target object are predicted in an anchor-free fashion by decomposing tracking into parallel classification and regression problems. The proposed anchor-free design obviates the need for hyperparameters related to the anchor boxes, making CAT more generic and flexible. Second, the proposed centerness-aware classification branch can identify the foreground from the background while predicting the normalized distance from the location within the foreground to the target center, i.e., the centerness. The proposed centerness-aware classification branch improves the tracking accuracy and robustness significantly by suppressing low-quality state estimates. The experiments show that our centerness-aware anchor-free tracker, with its appealing features, achieves salient performance in a wide variety of tracking scenarios.

Target-based terrestrial laser scan registration extended by target orientation

In almost all projects, in which terrestrial laser scanning is used, the scans must be registered after the data acquisition. Despite more and more new and automated methods for registration, the classical target-based registration is still one of the standard procedures. The advantages are obvious: independence from the scan object, the geometric configuration can often be influenced and registration results are easy to interpret. When plane black-and-white targets are used, the algorithm for estimating the target center fits a plane through the scan of a target, anyway. This information about the plane orientation has remained unused so far. Hence, including this information in the registration does not require any additional effort in the scanning process. In this paper, we extend the target-based registration by the plane orientation. We describe the required methodology, analyze the benefits in terms of precision and reliability and discuss in which cases the extension is useful and brings a relevant advantage. Based on simulations and two case studies we find out that especially for registrations with bad geometric configurations the extension brings a big advantage. The extension enables registrations that are much more precise. These are also visible on the registered point clouds. Thus, only a methodological change in the target-based registration improves its results.

A novel generalized grey target decision method with index and weight both containing mixed types of data

PurposeThis paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.Design/methodology/approachThe decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.FindingsThe certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.Research limitations/implicationsThe proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.Originality/valueThe proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.

Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates

Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson’s correlation coefficient to measure shared variability between simultaneously recorded units (32–112) and found significantly higher noise-correlation and positive correlation between the populations’ signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.

The Effect of Differential Attentional Focus Strategies on the Performance of Military Elite Shooters

Background and Objective. The effect of attentional focus strategies on performance has been an interesting area of investigation, especially when the precision of performance is of significance. The purpose of the present study is to investigate the effect of different attentional focus strategies on performance precision of elite military shooters. Methods. This study is semiexperimental with an intragroup design. A number of 10 military marksmen (30-42 years old) with at least 10 years of experience in shooting performed under four attentional focus strategies in a counterbalanced design. In each strategy, two blocks (each consisting of 20 trials) were conducted. Shooters’ performance was recorded using SCATT device and analyzed using the factorial variance analysis with repeated measure. Results. Results showed that the interactional effects of internal-external/relevant-irrelevant focuses of attention were significant on shooting record, shooting accumulation, and stability on the target center. Results suggest that the external-relevant attentional focus strategies were more effective than other focus strategies. Conclusion. The results of the study support the hypothesis that external-relevant attentional focus produced better scores, better accumulation, more stability at the target center, and less average fluctuation. Therefore, this attentional focus strategy improves performance precision of military elite shooters.

NEIGHBOUR-BASED DOMAIN ADAPTATION FOR INVESTIGATION OF TRANSFERABLE ABILITY OF PREVIOUSLY LABELED DATA FOR LAND-COVER CLASSIFICATION OF AERIAL IMAGES

Existing land-cover classification methods are usually based on adequate labelled data. But annotating enough training samples is hard and time-consuming. Therefore, we need to investigate how existing labelled data can help to increase land-cover classification. Source labelled data are proposed to be selected by calculating the target center of reliable target pseudo-labelled data for each class in this paper. Then we augment the training dataset with reliable target pesudo-labeled data and selected source labelled data to improve the quality and quantity of training dataset. We also investigate the amount of source labelled data that should be selected and the number of limited target labelled data that can produce good transfer learning performance. The UC Merced dataset is employed as the target dataset to evaluate the proposed approach while the NWPU-RESISC45 dataset is considered as the source labelled data. The experimental results show that selected source labelled data and reliable target pesudo-labeled data may improve the land-cover classification performance if selected source labelled data and reliable target pesudo-labeled data are augmented with the limited target labelled data respectively.

Noise-correlation is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates

Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain computer interfaces (BCIs) (Zhao et al. 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson’s correlation coefficient to measure shared variability between simultaneously recorded units (32 – 112) and found significantly higher noise-correlation and positive correlation between the populations’ signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted mean matched noise-correlation analysis in order to decouple known interactions between event triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning towards autonomous BCI updating.

ORTHOGRAPHIC REFLECTANCE IMAGE FOR PLANAR TARGET LOCALIZATION IN LOW DENSITY TLS POINT CLOUDS

<p><strong>Abstract.</strong> Point cloud registration is important and essential task for terrestrial laser scanning applications. Point clouds acquired at different positions exhibit significant variation in point density. Most registration methods implicitly assume dense and uniform distributed point clouds, which is hardly the case in large-scale surveying. The accuracy and robustness of feature extraction are greatly influenced by the point density, which undermines the feature-based registration methods. We show that the accuracy and robustness of target localization dramatically decline with decreasing point density. A methodology for localization of artificial planar targets in low density point clouds is presented. An orthographic image of the target is firstly generated and the potential position of the target center is interactively selected. Then the 3D position of the target center is estimated by a non-linear least squares adjustment. The presented methodology enables millimeter level accuracy of target localization in point clouds with 30mm sample interval. The robustness and effectiveness of the methodology is demonstrated by the experimental results.</p>