Weapon Detection for Particular Scenarios Using Deep Learning

Vállez, Noelia; Velasco-Mata, Alberto; Corroto, Juan Jose; Deniz, Oscar

doi:10.1007/978-3-030-31321-0_32

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…The second one, labelled as "Filtered (FP)", shows how only the pose-based FP filtering method (Subsection 4.4) significantly reduces the number of false positives. It can be observed that Precision met-Table 3: Evaluation results for detector 1 [18] and detector 2 [11] using the implementation 1 (Keras-TensorFlow). Precision and recall metrics have been calculated with a 0.5 confidence threshold.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In this work, two image-based handgun detectors have been used to test the proposed method. The first detector [18] is based on a Faster R-CNN architecture with a ResNet network backbone. The training dataset was composed of 871 images provided by the University of Seville [3], which were acquired from two CCTV cameras located in different college halls.…”

Section: Handgun Detectionmentioning

confidence: 99%

“…Evaluation results for detector 1[18] and detector 2[11] using the implementation 2 (Open-MMLab Detection). Precision and recall metrics have been calculated with a 0.5 confidence threshold.…”

mentioning

confidence: 99%

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Improving handgun detectors with human pose classification

Ruíz-Santaquiteria¹,

Déniz²,

Vállez³

et al. 2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

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Unfortunately, attacks with firearms such as handguns have become too common. CCTV surveillance systems can potentially help to prevent this kind of incidents, but require continuous human supervision, which is not feasible in practice. Image-based handgun detectors allow the automatic location of these weapons to send alerts to the security staff. Deep learning has been recently used for this purpose. However, the precision and sensitivity of these systems are not generally satisfactory, causing in most cases both false alarms and undetected handguns, particularly when the firearm is far from the camera. This paper proposes the use of information related to the pose of the subject to improve the performance of current handgun detectors. More concretely, a human full-body pose classifier has been developed which is capable of separating between shooting poses and other non-dangerous poses. The classified pose is then used to reduce both the number of false positives (FP) and false negatives (FN). The proposed method has been tested with several datasets and handgun detectors, showing an improvement under various metrics.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Handgun Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Improving handgun detectors with human pose classification

Ruíz-Santaquiteria¹,

Déniz²,

Vállez³

et al. 2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

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show abstract

“…To decrease the false-positive and false-negative rates, some authors have proposed methods such as using a symmetric dual camera system to improve the selection of candidate regions, thus increasing the performance of the model [11]. Another approach used is to model false positives as anomalies to filter false positives and increase the overall performance of the detector [12].…”

Section: Previous Workmentioning

confidence: 99%

Using human pose information for handgun detection

Velasco-Mata¹,

Ruíz-Santaquiteria²,

Vállez³

et al. 2021

Neural Comput & Applic

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Fast automatic handgun detection can be very useful to avoid or mitigate risks in public spaces. Detectors based on deep learning methods have been proposed in the literature to trigger an alarm if a handgun is detected in the image. However, those detectors are solely based on the weapon appearance on the image. In this work, we propose to combine the detector with the individual’s pose information in order to improve overall performance. To this end, a model that integrates grayscale images from the output of the handgun detector and heatmap-like images that represent pose is proposed. The results show an improvement over the original handgun detector. The proposed network provides a maximum improvement of a 17.5% in AP of the proposed combinational model over the baseline handgun detector.

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“…The new step will act as a filter able to recognize typical FPs of the detector in the particular scenario. Therefore, this problem can be seen as an anomaly detection problem where the anomalies are those detections that are not similar to the FPs modeled by the filter [7,8]. In fact, the anomalies detected on this step will be the real alarms.…”

Section: Introductionmentioning

confidence: 99%

Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

Self Cite

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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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Weapon Detection for Particular Scenarios Using Deep Learning

Cited by 4 publications

References 10 publications

Improving handgun detectors with human pose classification

Improving handgun detectors with human pose classification

Using human pose information for handgun detection

Deep autoencoder for false positive reduction in handgun detection

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