Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

Abruzzo, Benjamin; Carey, Kevin; Lowrance, Christopher J.; Sturzinger, Eric; Arnold, Ross; Korpela, Christopher

doi:10.1109/hst47167.2019.9032904

Cited by 11 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This scaling function is then applied to Efficient-Net B7. This Efficient-Net architecture is further used to form Efficient-Net, which used fused features with different resolutions for object detection [8].…”

Section: Efficient-net Architecturementioning

confidence: 99%

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Arif¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient-Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism. This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension. The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution. Efficient-Net algorithm gives better results than existing algorithms. By using Efficient-Net algorithms the accuracy achieved 98.12% when epochs increase as compared to other algorithms.

show abstract

Section: Efficient-net Architecturementioning

confidence: 99%

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Arif¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Human pose information has been recently used for handgun detection and threat assessment. Abruzzo et al [26] proposed a method for identifying people and handguns in images and then evaluate the threat level of the person poses based on the their body posture. However, the main limitation of this work is that the handgun detection performance is limited by the handgun detector used (in this case YOLO).…”

Section: Related Workmentioning

confidence: 99%

Handgun Detection Using Combined Human Pose and Weapon Appearance

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…Some other works proposed using information fusion to improve the detection of handguns in videos [18,25,2,1]. For example, the authors in [18] fused the binocular information to eliminate an important number of possible FP from the background in the frames.…”

Section: Related Workmentioning

confidence: 99%

“…However, the required setup is not available in regular video surveillance environments, i.e., two synchronized symmetric cameras set at a specific distance and orientation angles. In [1], the authors used the persons skeletal pose estimate to detect the threat in an image. They designed a multi-stage classification model.…”

Section: Related Workmentioning

confidence: 99%

“…The most recent studies applied preor post-processing techniques to further reducing the errors [16,26,4,19]. While other studies used data fusion to improve the overall performance [25,2,18,1,23]. However, none of these works tried to leverage the temporal information existent in the videos in order to mitigate the detection of false positives and hence reduce false alarms in handgun detection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

Olmos,

Tabik,

Perez-Hernandez

et al. 2021

Preprint

View full text Add to dashboard Cite

Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable. Using such detectors still implies a high number of false alarms and false negatives. In this context, most existent studies select one of the latest single-image detectors and train it on a better dataset or use some preprocessing, post-processing or data-fusion approach to further reduce false alarms. However, none of these works tried to exploit the temporal information present in the videos to mitigate false detections. This paper presents a new system, called MULTI Confirmation-level Alarm SysTem based on Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM) (MULTI-CAST), that leverages not only the spacial information but also the temporal information existent in the videos for a more reliable handgun detection. MULTICAST consists of three stages, i) a handgun detection stage, ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal confirmation stage. The temporal confirmation stage uses the positions of the detected handgun in previous instants to predict its trajectory in the next frame. Our experiments show that MULTI-CAST reduces by 80% the number of false alarms with respect to Faster R-CNN based-single-image detector, which makes it more useful in providing more effective and rapid security responses.

show abstract

Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People

Cited by 11 publications

References 10 publications

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Handgun Detection Using Combined Human Pose and Weapon Appearance

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

Contact Info

Product

Resources

About