Deep learning based fall detection is one of the crucial tasks for intelligent video surveillance systems, which aims to detect unintentional falls of humans and alarm dangerous situations. In this work, we propose a ...
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ISBN:
(纸本)9781665405409
Deep learning based fall detection is one of the crucial tasks for intelligent video surveillance systems, which aims to detect unintentional falls of humans and alarm dangerous situations. In this work, we propose a simple and efficient framework to detect falls through a single and small-sized convolutional neural network. To this end, we first introduce a new image synthesis method that represents human motion in a single frame. This simplifies the fall detection task as an image classification task. Besides, the proposed synthetic data generation method enables to generate a sufficient amount of training dataset, resulting in satisfactory performance even with the small model. At the inference step, we also represent real human motion in a single image by estimating mean of input frames. In the experiment, we conduct both qualitative and quantitative evaluations on URFD and AIHub airport datasets to show the effectiveness of our method.
Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of "smart city." video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of r...
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Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of "smart city." video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable;thus, the centralized framework is not sufficient for real-timeprocessing of media data needed for smart video surveillance. To tackle this problem, edge computing - a technique for accelerating the development of AIoT (AI across IoT) in smart cities - can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and low-throughput media data transmission.
This paper provides a video SAR processing scheme with flexible trajectories along with a brief introduction of the corresponding SAR backprojection approach. For this processing algorithm a Matlab model is introduced...
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ISBN:
(纸本)9783944976341
This paper provides a video SAR processing scheme with flexible trajectories along with a brief introduction of the corresponding SAR backprojection approach. For this processing algorithm a Matlab model is introduced and respective aspects for acceleration and robustification of the method are presented, making use of graphic core processing. A processing example of 4096x4096 image pixels with 16384 antenna positions is evaluated with which an acceleration factor of more than 430 is achieved compared to a Matlab implementation. Results of the backprojection implementation are provided with real flight data, acquired using HENSOLDT's Multifunction Surveillance Radar PrecISR (TM) 1000 and compared to an omega-k algorithm. A first example of a video SAR processing is finally presented.
Near-infrared fluorescence imaging in the second window has emerged as a valuable tool for the non-invasive and real-time assessment of vascular information in skin flaps. Enhancing flap images to provide more accurat...
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Near-infrared fluorescence imaging in the second window has emerged as a valuable tool for the non-invasive and real-time assessment of vascular information in skin flaps. Enhancing flap images to provide more accurate flap vascularization information is critical for predicting flap viability. To address the limitations of existing methods in enhancing vessel images, we propose a novel and adaptive technique for enhancing flap microvessel images. Multiple strategies can be employed to effectively enhance the visualization of small-scale vessels. Firstly, the proposed method leverages the multiscale rolling guided filter to acquire the base layer and detail layers at different scales. Furthermore, correlation coefficients are utilized to weigh and fuse the detail layers effectively. To suppress noise amplification while enhancing vascular structures, an improved adaptive gamma correction method based on local visual saliency is introduced. Meanwhile, the bilateral gamma correction is used to enhance the base layer. Finally, the enhanced base layer and detail layer are fused using the weighted fusion strategy. We conducted experiments on skin flap vessel images, retinal fundus images, finger vein images, and low-light images. Our method achieved excellent results in metrics such as NIQE, AMBE, and WPSNR, demonstrating significant advantages in preserving the structural integrity and brightness consistency of the images. The obtained results validate the potential of this method in enhancing vascular images, indicating promising prospects in the field of medicine.
This work proposes a Cyber-Physical System (CPS) for protecting smart electric grid critical infrastructures using video surveillance while remotely monitoring them. Due to the critical nature of the smart grid, it is...
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This work proposes a Cyber-Physical System (CPS) for protecting smart electric grid critical infrastructures using video surveillance while remotely monitoring them. Due to the critical nature of the smart grid, it is necessary to guarantee an adequate level of safety, security and reliability. Thus, this CPS is back-boned by a time-Sensitive Network solution providing concurrent support for smart video surveillance and smart grid control over a single communication infrastructure. To this end, TSN delivers high-bandwidth communication for video surveillance and deterministic quality of service, latency and bandwidth guarantees, required by the time-critical revision smart grid control. On the one hand, the CPS utilizes High-availability Seamless Redundancy in the control subsystem via Remote Terminal Units (RTUs) guaranteeing seamless failover against failures in smart grid. On the other hand, the smart video surveillance subsystem applies machine learning to monitor secured perimeters and detect people around the smart grid critical infrastructure. Moreover, it is also able to directly interoperate with RTUs to send alarms in case of for example, an intrusion. The work evaluates the accuracy and performance of the detection using common metrics in surveillance field. An integrated monitoring dashboard has also been developed in which all CPS information is available in realtime.
There is a natural distortion effect that seen from long-range imaging systems occurs in hot airs causes to see defocusing images. It is known as heat haze, heat scintillation, mirage or atmospheric turbulence. In ord...
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With the continuous development of Internet of Things technology, laboratory equipment management is gradually changing to the direction of intelligence and remote. In this paper, aiming at the data detection of labor...
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ISBN:
(纸本)9798400716607
With the continuous development of Internet of Things technology, laboratory equipment management is gradually changing to the direction of intelligence and remote. In this paper, aiming at the data detection of laboratory equipment, a solution of laboratory equipment image data patrol device based on Internet of Things technology is proposed. Through the acquisition, processing and transmission of equipment image data, the real-time monitoring and evaluation of equipment operation status and performance are realized. The research in this paper has certain reference value for improving the management efficiency and operation performance of laboratory equipment.
In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. Therefore, we have developed an online interactive sy...
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At present, most unmanned aerial vehicles (UAV) smoke detection systems transmit video back to the ground station computer for analysis to determine whether a fire has occurred, Since the image transmission process ta...
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video prediction requires efficient models capable of forecasting future frames which is a crucial task in various domains. However, many current methodologies are based on autoregressive mechanism, suffering from low...
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ISBN:
(纸本)9798350359329;9798350359312
video prediction requires efficient models capable of forecasting future frames which is a crucial task in various domains. However, many current methodologies are based on autoregressive mechanism, suffering from low computing efficiency, error propagation and difficulty in parallel processing of data. With an emphasis on efficiency, we propose the Spatio-Temporal Non-autoregressive Model (STNAM) designed for video prediction tasks. This model aims to achieve superior computational efficiency and reduced error accumulation compared to conventional methodologies. The STNAM is grounded in encoder-prediction-decoder framework with a Spatio-Temporal Attention and a Positional encoding. Experimental evaluations on benchmark video datasets showcase the efficacy of the proposed model. It demonstrates competitive performance in predicting video sequences, establishing its potential for real-timevideo forecasting applications.
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