Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning

Shirsat, Samit; Naik, Aakash; Tamse, Darshan; Yadav, Jaysingh; Shetgaonkar, Pratiksha; Aswale, Shailendra

doi:10.1109/vitecon.2019.8899441

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…The system's performance is compared to a sliding window proposal technique, and it is discovered that FRCNN and RCNN-based models trained on a dataset perform well. The algorithm can reliably anticipate crime occurrences even in low-quality films, yielding good results [5,20,31].…”

Section: B Automatic Hanggun Detection Using Machine Learningmentioning

confidence: 99%

Deep Learning Models for Crime Intention Detection Using Object Detection

Hashi¹,

Abdirahman²,

Elmi³

et al. 2023

IJACSA

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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.

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Section: B Automatic Hanggun Detection Using Machine Learningmentioning

confidence: 99%

Deep Learning Models for Crime Intention Detection Using Object Detection

Hashi¹,

Abdirahman²,

Elmi³

et al. 2023

IJACSA

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“…They are offered by cloud companies, but the data processing algorithms behind them are not public. They are also widely used in various fields of research, such as modern knowledge management solutions [22] and criminal detection and recognition [23]. The cognitive API is an application program interface via the HTTP/REST protocol [24], with which developers can easily integrate powerful recognition features in their own applications.…”

mentioning

confidence: 99%

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Chen

Saiki

Nakamura

2020

Sensors

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To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.Our interest is to use little image data, applying cognitive services to implement affordable context sensing that can adapt to custom contexts in every single house. The cognitive service is a cloud service with cognitive computing functions that provide the capability to understand multimedia data (i.e., vision (object recognition) [19], speech recognition [20], natural language processing [21], etc.), based on sophisticated machine-learning algorithms powered by the offered big data and large-scale computing. They are offered by cloud companies, but the data processing algorithms behind them are not public. They are also widely used in various fields of research, such as modern knowledge management solutions [22] and criminal detection and recognition [23]. The cognitive API is an application program interface via the HTTP/REST protocol [24], with which developers can easily integrate powerful recognition features in their own applications. An image-based cognitive API receives an image from an external application, i.e., extracts specific information from the image, and returns the information in JavaScript Object Notation (JSON) [25] format from the cloud server rather than the local. The information usually contains a set of words called "tags", representing objects and concepts that the API has recognized in the given image. Examples of tags from the API are: [Living, room, indoors, classroom, basement, supporting structure]. The information of interest and the way of recognizing the image vary among individual cognitive services. Related work uses image tagging technology with deep learning, as in [26][27][28], but the implementation is more complex. In our future realistic implementation, for security and privacy of the users the images are not saved after sending the APIs over.The main contribution of this paper is to present a novel method which is not only affordable but also has higher accuracy in recognizing fine-grained home contexts. For this purpose, we are currently investigating techniques that integrate inexpensive camera devices, multiple image-based cognitive APIs, and light-weight machine learning. We previously encoded the tags of a single API to document vectors, then applied them into machine learning for the model construction [29]. However, we found that the accuracy significantly decreased for contexts with multiple people (e.g., "...

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Bringing the Edge Home: Edge Computing in the Era of Emerging WLANs

Edirisinghe,

Ranaweera

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning

Cited by 13 publications

References 18 publications

Deep Learning Models for Crime Intention Detection Using Object Detection

Deep Learning Models for Crime Intention Detection Using Object Detection

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Bringing the Edge Home: Edge Computing in the Era of Emerging WLANs

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