Object Detection for Crime Scene Evidence Analysis Using Deep Learning

Saikia, Surajit; Fidalgo, Eduardo; Alegre, Enrique; Fernández-Robles, Laura

doi:10.1007/978-3-319-68548-9_2

Cited by 47 publications

(17 citation statements)

References 20 publications

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“…Another study that demonstrated the high performance of Faster R-CNN for video analysis in real time is [32], in which the video was processed at a rate of 110 frames per second. Another interesting study is [33], in which the authors made a system based on Faster R-CNN for the real-time detection of evidence in crime scenes. One last study to highlight is [34], in which the authors created an augmented reality based on Faster R-CNN implementation using a gaming laptop.…”

Section: Related Work In Crime Events Video Detectionmentioning

confidence: 99%

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

et al. 2019

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This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as “objects” to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.

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Section: Related Work In Crime Events Video Detectionmentioning

confidence: 99%

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

et al. 2019

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“…We have created a testset "Image Net-Room Objects" for evaluating this system comprising of images commonly found in an indoor environment, and accuracy is achieved at the end. [3][8] Sachee Nene (2017), Deep learning is the most emerging field of machine learning and has shown extensive contribution in various research areas. It has helped in overcoming the drawbacks of traditional methods by making the systems less complex and fast.…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on Image Dimensionality Reduction Using Deep Learning Techniques

Monica¹,

Bindu²,

Sridevi³

2018

IJET

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Images provide rich information. With reference to the data set which may be related or unrelated in nature, locates step by step, a wide range of application and its attributes through capturing mechanism by sensing the suitable technologies. On the other hand, it also creates a huge quantity of data which may be relevant, irrelevant or redundant in nature and it is used for detailed task of the image. Also, Many brings a lot of problems such as increase in computational time of image, density of image and range of mapping of data, semantics of the data set and also it also there is a scope of huge amount of labeled data for the process of training to the new environment setup. Mostly, this is not easy and costly for users to obtain sufficient training models in several application modules. This research paper deals with these problems by exploring the more classical dimension reduction algorithms with deep knowledge for supporting communities.

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“…Thus, multiple objects can be detected and classified within an image with high performance. Image classification techniques allow for the identification of specific content [ 8 ], which can be further explored through object detection [ 9 , 10 , 11 ] and object recognition techniques [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

Blanco‐Medina

Fidalgo

Alegre

et al. 2020

Sensors

Self Cite

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Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.

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Object Detection for Crime Scene Evidence Analysis Using Deep Learning

Cited by 47 publications

References 20 publications

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

Survey on Image Dimensionality Reduction Using Deep Learning Techniques

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

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