Detecting Criminal Activities and Promoting Safety Using Deep Learning

Mathur, Rohan; Chintala, Tejas; Rajeswari, D

doi:10.1109/accai53970.2022.9752619

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this case, to improve the efficiency of the security management system and minimize crime incidents and losses, an effective crime prediction analysis system is needed. Such a system would enable proactive crime prevention and ensure robust security management in public places such as banks, shopping malls, and avenues [3,6].…”

Section: Introductionmentioning

confidence: 99%

“…In contemporary times, machine-learning and advanced image-processing algorithms have significantly contributed to the evolution of smart surveillance and security systems, as evidenced by recent developments [3,6,7,8]. In addition, the rise of smart devices and networked cameras has also boosted this field.…”

Section: Introductionmentioning

confidence: 99%

“…A deep learning neural network generally has five layers: input and output layers with Convolution, Max-Pooling, and Fully connected layers. Many individuals choose to employ pre-trained deep learning models due to constraints such as limited time, memory, and resources such as CPU and processors [6,35]. When opposed to machine learning, which involves explicit design, these pre-trained models produce better and more accurate outcomes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Models for Crime Intention Detection Using Object Detection

Hashi¹,

Abdirahman²,

Elmi³

et al. 2023

IJACSA

View full text Add to dashboard Cite

The majority of visual based surveillance applications and security systems heavily rely on object detection, which serves as a critical module. In the context of crime scene analysis, images and videos play an essential role in capturing visual documentation of a particular scene. By detecting objects associated with a specific crime, police officers are able to reconstruct a scene for subsequent analysis. Nevertheless, the task of identifying objects of interest can be highly arduous for law enforcement agencies, mainly because of the massive amount of data that must be processed. Hence, the main objective of this paper is to propose a DL-based model for detecting tracked objects such as handheld firearms and informing the authority about the threat before the incident happens. We have applied VGG-19, ResNet, and GoogleNet as our deep learning models. The experiment result shows that ResNet50 has achieved the highest average accuracy of 0.92% compared to VGG19 and GoogleNet, which have achieved 0.91% and 0.89%, respectively. Also, YOLOv6 has achieved the highest MAP and inference speed compared to the faster R-CNN.

show abstract

Section: Introductionmentioning

confidence: 99%