Vision and Crowdsensing Technology for an Optimal Response in Physical-Security

Enríquez, Fernando; Soria, L.; Álvarez-García, Juan Antonio; Sancho-Caparrini, Fernando; Velasco, Francisco; Deniz, Oscar; Vállez, Noelia

doi:10.1007/978-3-030-22750-0_2

Cited by 9 publications

(9 citation statements)

References 24 publications

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“…Over the past few decades, many researchers have addressed the evacuation of buildings and crowded places [15][16][17][18][19][20]. Models implemented in these works, although having their own characteristics, have in common some techniques for modeling agent behavior and their interaction.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Hybrid agent-based methodology for testing response protocols

López-Ortiz

Sancho-Caparrini

Amor

et al. 2021

Knowledge-Based Systems

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In recent years we have seen multiple incidents with a large number of people injured and killed by one or more armed attackers. Since this type of violence is difficult to predict, detecting threats as early as possible allows to generate early warnings and reduce response time. In this context, any tool to check and compare different action protocols can be a further step in the direction of saving lives. Our proposal combines features from continuous and discrete models to obtain the best of both worlds in order to simulate large and crowded spaces where complex behavior individuals interact. With this proposal we aim to provide a tool for testing different security protocols under several emergency scenarios, where spaces, hazards, and population can be customized. Finally, we use a proof of concept implementation of this model to test specific security protocols under emergency situations for real spaces. Specifically, we test how providing some users of a university college with an app that informs about the type and characteristics of the ongoing hazard, affects in the safety performance.

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Section: Motivation and Related Workmentioning

confidence: 99%

Hybrid agent-based methodology for testing response protocols

López-Ortiz

Sancho-Caparrini

Amor

et al. 2021

Knowledge-Based Systems

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“…For the particular problem of weapon (handgun and knife) detection, [47] reviews recent work and shows that Faster R-CNN has been the prevalent method. For that reason, we have selected the Faster R-CNN architecture to train a handgun detector with a dataset provided by the University of Seville [1]. The dataset is composed of 871 images that contain 177 annotated handguns.…”

Section: Handgun Detectormentioning

confidence: 99%

“…Weapons, among other threats, need to be detected as soon as possible to eliminate or mitigate the danger they could cause [1]. Traditionally, the surveillance of public scenarios has been accomplished by the human supervision of the images captured by closed-circuit television (CCTV) systems.…”

Section: Introductionmentioning

confidence: 99%

Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

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In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use the k-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system’s precision of 1.2%$$-47$$ - 47 % when the autoencoder is applied.

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“…Related studies in this area show that early detection of security threats or risks is fundamental to mitigate the damage caused as much as possible [3]. Situations involving firearms such as handgun attacks, mass shootings, gunfire incidents on school grounds [4] or terrorist attacks [5] are representative examples of this kind of threats, which unfortunately have become rather common nowadays.…”

Section: Introductionmentioning

confidence: 99%

Handgun Detection Using Combined Human Pose and Weapon Appearance

et al. 2021

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Closed-circuit television (CCTV) systems are essential nowadays to prevent security threats or dangerous situations, in which early detection is crucial. Novel deep learning-based methods have allowed to develop automatic weapon detectors with promising results. However, these approaches are mainly based on visual weapon appearance only. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible. In this work, a novel method is proposed to combine, in a single architecture, both weapon appearance and human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed in different subnetworks and combined to produce the handgun bounding box. Results obtained show that the combined model improves the handgun detection state of the art, achieving from 4.23 to 18.9 AP points more than the best previous approach. INDEX TERMSCCTV surveillance, deep learning, handgun detection, human pose estimation.

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Vision and Crowdsensing Technology for an Optimal Response in Physical-Security

Cited by 9 publications

References 24 publications

Hybrid agent-based methodology for testing response protocols

Hybrid agent-based methodology for testing response protocols

Deep autoencoder for false positive reduction in handgun detection

Handgun Detection Using Combined Human Pose and Weapon Appearance

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