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In both military and civilian surveillance systems such as Air Traffic Control (ATC) systems, tracking targets in clutter using radar involves dealing with a number of challenges, all related to real time decision and data fusion ...
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In both military and civilian surveillance systems such as Air Traffic Control (ATC) systems, tracking targets in clutter using radar involves dealing with a number of challenges, all related to real time decision and data fusion theories. A major challenge is to detect the real targets through the received measurements in real time and to activate the tracking process.
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We describe the theory of a detect-track-confirm filter whose role is moving target detection and clutter suppression in surveillance data. The filter has broad generality due to the minimal assumptions made in developing the theo...
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We describe the theory of a detect-track-confirm filter whose role is moving target detection and clutter suppression in surveillance data. The filter has broad generality due to the minimal assumptions made in developing the theory. Track confirmation is decided on the basis of a probability measure that is fully computable from clutter properties measured from surveillance data, without needing to assume target properties such as trajectory or detectability. Experimental results on real surveillance datasets are presented.
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Theoretical design of a tracker with respect to the so-called target perceivability is presented. Basic rules and analytic formulas for the determination of tracker parameters are presented for making better tracking decisions (i....
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Theoretical design of a tracker with respect to the so-called target perceivability is presented. Basic rules and analytic formulas for the determination of tracker parameters are presented for making better tracking decisions (i.e., track initiation, confirmation, and termination) toward enhancing tracking performance. Simulation results are provided that support the theoretical design and demonstrate the enhancement of the tracker in tracking performance
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Research by Rajsic. Wilson, and Pratt (2015. 2017) suggests that people are biased to use a target-confirming strategy when performing simple visual search. In 3 experiments, we sought to determine whether another stubborn phenome...
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Research by Rajsic. Wilson, and Pratt (2015. 2017) suggests that people are biased to use a target-confirming strategy when performing simple visual search. In 3 experiments, we sought to determine whether another stubborn phenomenon in visual search, the low-prevalence effect (Wolfe. Horowitz. & Kenner, 2005), would modulate this confirmatory bias. We varied the reliability of the initial cue: For some people, targets usually occurred in the cued color (high prevalence). For others. targets rarely matched the cues (low prevalence). High cue-target prevalence exacerbated the confirmation bias. indexed via search response times (RTs) and eye-tracking measures. Surprisingly, given low cue-target prevalence, people remained biased to examine cue-colored letters, even though cue-colored targets were exceedingly rare. At the same time. people were more fluent at detecting the more common, cue-mismatching targets. The findings suggest that attention is guided to "confirm" the more available cued target template, but prevalence learning over time determines how fluently objects are perceptually appreciated.
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This paper presents a novel computational imaging method for object tracking. The proposed slit-type one-dimensional brightness distribution sensor (Obrid-Sensor) is designed to be applied to detect falls, localize, and track subj...
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This paper presents a novel computational imaging method for object tracking. The proposed slit-type one-dimensional brightness distribution sensor (Obrid-Sensor) is designed to be applied to detect falls, localize, and track subjects without invading privacy. According to the vertical and horizontal one-dimensional brightness distributions acquired by the proposed sensors, the subject's motion is monitored without images or videos. The effectiveness of the proposed method to detect falls, localize, and track is verified by experiment. The motion detection and location can be implemented without requiring images or videos, which may invade privacy. The detection range of the proposed privacy-preserving sensor is also confirmed. Finally, the proposed sensor has a simpler structure than the traditional Obrid-Sensor, making it attractive owing to its simple design, facile application, and low cost. (C) 2016 The Authors. Published by Elsevier Ltd.
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We present a liquid-level measurement system based on second-confirm recognition algorithm. Compared with the traditional methods of the liquid-level measurement system, which has low accuracy, slow measurement speed, and limited ...
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We present a liquid-level measurement system based on second-confirm recognition algorithm. Compared with the traditional methods of the liquid-level measurement system, which has low accuracy, slow measurement speed, and limited range of measurements, this system can accurately and quickly measure the liquid level. Moreover, it can automatically complete the measurement of the liquid-level line position. The main component of the system includes a vision detection system and a tracking control system. The vision detection system mainly includes image preprocessing, numeral recognition, and liquid-level line recognition. Among the vision detection system, numeral recognition is the key task. Therefore, we propose a numeral recognition algorithm that employs a second-confirm algorithm to recognize numerals. The algorithm first utilizes template matching to find one numeral from the image. Afterward, we can divide the numeral image out of the rule image. This can greatly optimize the issues of time-consumption. The numerals can be segmented from the numeral image when the numeral image is obtained. After the numerals are captured, the numerals and template image difference is computed to complete numeral recognition. In addition, tracking control system tracks the liquid line in real time through the level value feedback by the vision system, which calculates the position of the liquid level in the image and feeds back the level value to the tracking control system. After experimental verification, the system can meet the expected requirements of liquid-level measurement. (C) 2018 SPIE and IS&T
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Tracking pedestrians based on visual sensors has many diverse applications, among them autonomous driving. Through tracking, not only the position of pedestrians but also their temporal movement can be obtained. This information h...
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Tracking pedestrians based on visual sensors has many diverse applications, among them autonomous driving. Through tracking, not only the position of pedestrians but also their temporal movement can be obtained. This information helps vehicles and robots to sense their surrounding environment and thus they can plan appropriate reactions. In addition to obtaining a high recall, maintaining the consistency of tracked trajectories during data association is one of the most crucial issues of any tracker. Following the tracking-by-detection paradigm, a new method called 3D-TLSR (3D pedestrian tracking using local structure refinement) is presented in this thesis aiming at improving the accuracy, reliability, and consistency of tracked trajectories. The contributions of this work are fourfold. First, a framework combining both, 2D image and 3D object space information, to track multiple pedestrians in 3D object space is presented, in which tracking, detection, and prediction are all considered and improved to enhance tracking results in terms of completeness, correctness, and reliability. Second, a hierarchical association approach is introduced to improve the consistency of trajectories by utilising geometry cues, which is carried out in two steps: (1) targets whose assignments have a high probability of correctness are selected as anchors and (2) prior knowledge about the geometry changes of the anchors is used to correct unreliable assignments of detections with their nearby trajectories in 3D space. Additionally, the tracking-to-confirm-detection (TCD) approach is introduced to address low-quality detection results so that both, completeness and correctness of trajectories, can be improved during tracking. Third, a simple approach to estimate and correct the velocity of a tracked person is proposed based on the relationship of moving targets, which allows missed detections to be better retrieved. Fourth, a new dataset called MuVi, consisting of image sequences of pedestrians from three different viewpoints with a large overlapping has been acquired, which can be employed for either single view or multi-view collaborative tracking. The new dataset offers additional data for the community to promote research achievements theoretically and practically. Experiments on different datasets are carried out to illustrate the advantages and weaknesses of the proposed tracking method and its individual component. Experimental results on the well known KITTI tracking benchmark, the ETHMS dataset, as well as a self-generated MuVi dataset show that the proposed tracker yields comparable results to other state-of-the-art methods and gives the best online result among all investigated approaches. On the ETHMS dataset, our approach obtains the best results with large margins for most tracking metrics. These findings confirm the effectiveness and generalization potential of the proposed tracking method.
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Marketers are concerned about how consumers' readings of product reviews and product rating statistics affect product evaluations.
Previous studies generally regard product review content and rating statistics as affecting produc...
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Marketers are concerned about how consumers' readings of product reviews and product rating statistics affect product evaluations.
Previous studies generally regard product review content and rating statistics as affecting product evaluations independently.
This may not be true. Typically, consumers enter a review section with pre-existing
positive expectations about a
product. When they see a negative average rating in this section, their expectancies are disconfirmed. Their surprise stemming
from the disconfirmation makes them read the reviews more carefully. Thus, review reading affects product evaluation more
strongly when viewing a negative average rating. Conversely, a positive average rating results in expectancy confirmation, which
weakens the effect of review reading. An eye-tracking
experiment using a mock-resort
review section supported these predictions.
These findings have implications for marketers in behavioral advertising, responding to reviews, and consumers writing
reviews.
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摘要 :
Online product review forums commonly provide consumers with averages of product ratings given by reviewers. Some also provide frequency distribution of ratings in the form of a histogram. The authors argue that consumers use thes...
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Online product review forums commonly provide consumers with averages of product ratings given by reviewers. Some also provide frequency distribution of ratings in the form of a histogram. The authors argue that consumers use these statistics as search aids when reading reviews. Provision of the average rating statistic affects consumers' reading of reviews through confirmation or disconfirmation of their expectancies about the product in question. However, provision of the distribution statistics attenuates this effect because it alerts consumers to divergent opinions. An eye-tracking experiment that simulated a forum visit demonstrates these effects. The experiment also shows that as a result of these effects, average rating provision causes consumer attitude toward the product to be more extreme, whereas rating distribution provision reduces this polarization. Further, supporting the expectancy confirmation account, results show that consumers with a high need for cognition exhibit a greater response to average rating provision than those with a low need. The findings suggest the benefits to marketers of tracking the number of negative reviews read by consumers and displaying positive reviews and negative reviews separately. The findings also identify circumstances that call for greater effort by marketers when responding to reviews. (C) 2019 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved.
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