Visual detection with search: an empirical model

Waldman, Gary S.; Wootton, John; Hobson, Gregory

doi:10.1109/21.97453

Cited by 25 publications

(13 citation statements)

References 11 publications

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“…Search performance is usually expressed as the cumulative detection probability as a function of time, and approximated by [35][36][37][38][39][40][41] …”

Section: Resultsmentioning

confidence: 99%

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Image dataset for testing search and detection models

Toet

2001

Opt. Eng

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Abstract. The TNO Human Factors Search -2 image dataset consists of: a set of 44 high-resolution digital color images of different complex natural scenes, the ground truth corresponding to each of these scenes, and the results of psychophysical experiments on each of these images. The images in the Search -2 dataset are a subset of a larger set that has been used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. The image dataset, an Excel file with the ground truth and observer data, and an extensive report describing the dataset are available on CD-ROM (requests by email to the first author). The dataset can be used to develop and validate digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and models of human visual search and detection. The dataset has already been used in more than ten different studies in the literature, ranging from studies evaluating target detectability metrics to eye movement studies and attempts to model the human visual system. In addition to this work, eight other articles in this special section address the Search -2 dataset.

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“…Search performance is usually expressed as the cumulative detection probability as a function of time, and approximated by [35][36][37][38][39][40][41] …”

Section: Resultsmentioning

confidence: 99%

“…[21][22][23][24] Models of the human visual search and detection capability that predict the detection probability for targets in complex ͑natural͒ scenes as a function of time ͑e.g., Visdet, 25 Oracle, 26 and GTV…”

Section: Computing Target Distinctnessmentioning

confidence: 99%

Image dataset for testing search and detection models

Toet

2001

Opt. Eng

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“…The procedure of the search experiment is described in (Toet et al, 1998(Toet et al, , 2001. Search performance is usually expressed as the cumulative detection probability as function of time, and it can be approximated by Krendel and Wodinsky (1960), Rotman et al (1989) and Waldman et al (1991):…”

Section: Model Evaluationmentioning

confidence: 99%

Information visibility using transmission methods

García

Rodrı́guez-Sánchez

Fdez-Valdivia

et al. 2010

Pattern Recognition Letters

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“…King et al [32] use realistic imagery and assumed an SDT model to describe the performance of the subjects. Waldman et al [33] developed an empirical model for visual detection with search, based on identifying parameters of a probabilistic model, such as search time and accuracy. Sperling [34] investigated visual search with attention more specifically looking at reaction times, and investigating a so-called Attention Operating Characteristic (AOC).…”

Section: A Decision Modelsmentioning

confidence: 99%

Operator Choice Modeling for Collaborative UAV Visual Search Tasks

Bertuccelli

Cummings

2012

IEEE Trans. Syst., Man, Cybern. A

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Abstract-Unmanned Aerial Vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real-time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models that can be used to develop assistive decision support for UAV operators involved in these complex search missions. Previous humanin-the-loop experiments have shown that operator detection probabilities may decay with increased search time. Providing the operators with the ability to requeue difficult images with the option of relooking at targets later was hypothesized to help operators improve their search accuracy. However, it was not well understood how mission performance could be impacted by operators performing requeues with multiple UAVs. This work extends a queueing model of the human operator by developing a retrial queue model (ReQM) that mathematically describes the use of relooks. We use ReQM to generate performance predictions through discrete event simulation. We validate these predictions through a human-in-the-loop experiment that evaluates the impact of requeueing on a simulated mutiple UAV mission. Our results suggest that while requeueing can improve detection accuracy and decrease mean search times, operators may need additional decision support to use relooks effectively.

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Visual detection with search: an empirical model

Cited by 25 publications

References 11 publications

Image dataset for testing search and detection models

Image dataset for testing search and detection models

Information visibility using transmission methods

Operator Choice Modeling for Collaborative UAV Visual Search Tasks

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