An Intelligent Camera Surveillance System with Effective Notification Features
01 Nov 2020-International Journal of Computing and Digital Systems (University of Bahrain)-Vol. 9, Iss: 6, pp 1251-1261
TL;DR: The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems and integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder.
Abstract: The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems. Other than regular functionalities such as motion detection, object detection, face recognition and counting people, it also integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder. When people are detected, a counting module displays the number of persons present in the surveillance area. A face recognition module distinguishes between authorised and unauthorised users. This biometric functionality reduces false alarms which makes the system more robust. An object detection module detects certain valuable objects such as handbags, laptops and smartphones. Also, images and short video recordings are stored on the cloud. Furthermore, the system introduces innovative real-time notification approaches for surveillance systems such as WhatsApp messages and phone calls, in addition to SMS and emails. Thus, this system is reliable and meets the aim of a modern intelligent surveillance system by combining multiple approaches to detect intrusions and to inform users effectively.
Citations
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10 Aug 2022
TL;DR: The intent and purpose of this research is how the metaverse is linked to the Blockchain method, by paying attention to data security in privacy, with this concept, the distribution of data will be safer because it uses the blockchain method.
Abstract: Metaverse is a world that is being developed now with the metaverse, the virtual world will develop rapidly because it creates a separate world, the metaverse relationship with the blockchain is created because there is a distribution of data with the blockchain method, the distribution of data will be better because the data will be sent securely using the data privacy process. The method used in this research is to use the literature review method and literature study, with the previous literature, it can be ascertained that this research is the latest research, because it has novelty based on previous research, therefore this research will be strong because it can find problems previous research that has been done. The intent and purpose of this research is how the metaverse is linked to the blockchain method, by paying attention to data security in privacy, with this concept, the distribution of data will be safer because it uses the blockchain method. In this research, how to come up with a system and try several aspects that exist in metaforce and blocking to produce a measurable impact.
2 citations
05 Nov 2018
TL;DR: This work comes up with an idea of smart surveillance system designed such a way that the user is notified when upcoming dangers are detected using image processing algorithms.
Abstract: Surveillance system plays a key role in maintaining the security in today's life. But, fail to provide the feature of avoiding the unfortunate happenings. This work comes up with an idea of smart surveillance system designed such a way that the user is notified when upcoming dangers are detected. In this work, motion of the intruders and the presence of a human faces are detected using image processing algorithms. As the motion detected in the locality of the surveillance area, a short video clip is recorded and sent to the user along with an alert message through the email server controlled by Raspberry Pi. When the cloud is not available, the data is stored locally in the Raspberry Pi and is sent to the user when the connection restarts. Live streaming video from the camera is accessed on any Internet enabled device. Surveillance camera rotates with the help of a DC motor. Also, temperature and gas sensors are integrated with the system.
1 citations
DOI•
01 Jan 2022
TL;DR: In this article, a survey of various SEDM strategies and tools that were developed earlier which captures the suspicious events in campuses or societies is discussed that provides a Full-proof secure environment.
Abstract: Safety and health security are the major concerns in today's modern world. Most of the countries have adopted camera surveillance systems to achieve a secure environment, one such example is restriction imposed for the movement of people during the COVID-19 pandemic. Thus, surveillance systems serve the purpose of humans to identify intruders with suspicious behavior. Detecting these intrusions or any suspicious events in an early stage from surveillance systems is an important and challenging task. This can be done using Suspicious Event Detection Models (SEDM) and tools. In earlier systems, it is found that machine learning methods are proved to be efficient in predicting the suspicion activities. In this chapter, a survey of various SEDM Strategies and Tools that were developed earlier which captures the suspicious events in campuses or societies is discussed that provides a Full-proof secure environment. Few, earlier SEDM have also used deep learning approaches, IoT, and fuzzy logic techniques. Finally, an improved SEDM for campuses based on deep learning is suggested. The capability of deep learning (CNN) method is very influential in extraction of features from unstructured contents, especially from captured images of video. The efficiency of this suggested SEDM will be better when compared with earlier state-of-art-systems, which do not support alarming system by making use of GPS, and then extracting personal details.
References
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27 Jun 2016
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
27,256 citations
"An Intelligent Camera Surveillance ..." refers methods in this paper
...However, CNN is a slow algorithm compared to other detection algorithms such as YOLO [25][26][27], SPPnet [14], Faster R-CNN [22] and SSD [20]....
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Posted Content•
TL;DR: The authors present some updates to YOLO!
Abstract: We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster. As always, all the code is online at this https URL
12,770 citations
TL;DR: SSD as mentioned in this paper discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, and combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
Abstract: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For $300\times 300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for $500\times 500$ input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at this https URL .
12,678 citations
Posted Content•
TL;DR: YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories, is introduced and a method to jointly train on object detection and classification is proposed, both novel and drawn from prior work.
Abstract: We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that don't have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes; it predicts detections for more than 9000 different object categories. And it still runs in real-time.
8,505 citations
"An Intelligent Camera Surveillance ..." refers methods in this paper
...The frame is first processed for object detection using the YOLO algorithm [26] whereby individuals and objects are detected....
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...However, CNN is a slow algorithm compared to other detection algorithms such as YOLO [25][26][27], SPPnet [14], Faster R-CNN [22] and SSD [20]....
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06 Sep 2014
TL;DR: This work equips the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement, and develops a new network structure, called SPP-net, which can generate a fixed-length representation regardless of image size/scale.
Abstract: Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. 224×224) input image. This requirement is “artificial” and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101.
3,945 citations
"An Intelligent Camera Surveillance ..." refers methods in this paper
...However, CNN is a slow algorithm compared to other detection algorithms such as YOLO [25][26][27], SPPnet [14], Faster R-CNN [22] and SSD [20]....
[...]