video target tracking has a wide range of application value in the field of automatic driving, UAV target tracking, security monitoring, etc. How to maintain stable tracking of the target among video data frames is th...
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Change detection is crucial for various industrial applications. Although image change detection datasets are abundant, the collection of labeled video data is time-consuming, expensive, and cumbersome. This scarcity ...
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Advanced computer vision technologies enable nearly real-time intelligent monitoring of homes by detecting anomalies such as falls and incidents of domestic violence, thereby enhancing home safety. Leveraging affordab...
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ISBN:
(纸本)9798350367331;9798350367348
Advanced computer vision technologies enable nearly real-time intelligent monitoring of homes by detecting anomalies such as falls and incidents of domestic violence, thereby enhancing home safety. Leveraging affordable home cameras and cloud computing services, this technology offers significant societal benefits. However, privacy concerns present challenges for its deployment in real-world settings. This paper introduces a method for anomaly detection in encrypted bitstream videos. By analyzing the video compression standard, we incorporate new appearance information that captures variations in object sizes alongside existing motion information, enhancing anomaly detection capabilities. We also refine the feature extraction process to reduce the impact of noise. Finally, we employ Gaussian Mixture Model to model the probability distribution of the data for effective anomaly detection. Experimental results demonstrate that our proposed privacy-preserving anomaly detection method achieves a commendable balance between protecting privacy and maintaining high detection performance.
In order to achieve the possibility and probability of discovering the violation of operation tasks through previous data, this paper proposes an intelligent identification algorithm for safety risk of transmission li...
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Due to the rapid expansion of Internet of Things (IoT) devices, massive amounts of data are generated daily, and the number of IoT devices will continue to increase. Cloud computing provides storage, processing, and a...
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ISBN:
(纸本)9781728190549
Due to the rapid expansion of Internet of Things (IoT) devices, massive amounts of data are generated daily, and the number of IoT devices will continue to increase. Cloud computing provides storage, processing, and analysis services for managing such large amounts of data. However, there are limitations concerning the network connectivity between the cloud and IoT devices. Nonetheless, the cloud platform has evident concerns and limitations in terms of responsiveness, latency, and overall performance for processing and accessing IoT traffic data. This process takes time, especially for large datasets, as there is back-and-forth communication between the client and the cloud. This increase in latency and energy consumption is unacceptable for real-time applications such as online gaming, smart health, video surveillance, etc. Hence the possibility of using Edge/Fog computing for optimal resource allocation. Our work aims to propose a system model for surveillance video analysis in an IoT 6G environment based on enhanced cloud architecture, Edge/Fog computing while developing an analytical model to satisfy QoS requirements (latency, energy consumption, etc.).
Fuzzy image target classification detection plays an important role in imageprocessing. Traditional classification detection methods are easily affected by environmental and equipment factors, and there are certain l...
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video forgery detection is crucial to combat misleading content, ensuring trust and credibility. Existing methods encounter challenges such as diverse manipulation techniques, dataset variation, real-timeprocessing d...
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video forgery detection is crucial to combat misleading content, ensuring trust and credibility. Existing methods encounter challenges such as diverse manipulation techniques, dataset variation, real-timeprocessing demands, and maintaining a balance between false positives and negatives. The research focuses on leveraging a Two-Layer Hybridized Deep CNN classifier for the detection of video forgery. The primary objective is to enhance accuracy and efficiency in identifying manipulated content. The process commences with the collection of input data from a video database, followed by diligent data pre-processing to mitigate noise and inconsistencies. To streamline computational complexity, the research employs key frame extraction to select pivotal frames from the video. Subsequently, these key frames undergo YCrCb conversion to establish feature maps, a step that optimizes subsequent analysis. These feature maps then serve as the basis for extracting significant features, incorporating Haralick features, Local Ternary Pattern, Scale-Invariant Feature Transform (SIFT), and light coefficient features. This multifaceted approach empowers robust forgery detection. The detection is done using the proposed Two-Layer Hybridized Deep CNN classifier that identifies the forged image. The outputs are measured using accuracy, sensitivity, specificity and the proposed Two-Layer Hybridized Deep CNN achieved 96.76%, 96.67%, 96.21% for dataset 1, 96.56%, 96.79%, 96.61% for dataset 2, 95.25%, 95.76%, 95.58% for dataset 3, which is more efficient than other techniques.
The growing demand for immersive Virtual reality (VR) experiences necessitates seamless stitching of panoramic video. VR unlocks the potential for applications like deep-sea exploration and self-driving car visualizat...
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The huge computation burden of state-of-the-art video coding technologies can be mitigated with Region-of-Interest (ROI) techniques that limit the highest coding effort to salient regions. However, the complexity over...
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ISBN:
(纸本)9789464593617;9798331519773
The huge computation burden of state-of-the-art video coding technologies can be mitigated with Region-of-Interest (ROI) techniques that limit the highest coding effort to salient regions. However, the complexity overhead of saliency detection can easily cancel out the speed gain of ROI coding. This work introduces a lightweight ROI tracking technique that can be used in place of compute-intensive ROI detection to guide a video encoder in inter coding. Low computational overhead is achieved by feeding motion vectors (MVs) of a video encoder back to our neural network that is trained for accurate estimation of ROI movement and size changes. The network training is carried out with our new dataset that is also released in this work to foster the development of head tracking techniques in applications like video conferencing. Our experimental results demonstrate substantial speedups with minimal accuracy trade-offs over traditional salient object detection (SOD) methods. In scenarios, where a single ROI is tracked with a 64-frame detection interval, our solution obtains up to 50-fold speedup with accuracy of 87% and an average ROI center error of 16 pixels. These results confirm that our ROI tracking approach is a potential technique for low-cost and low-power streaming media applications.
Nowadays, numerous encryption schemes have been proposed in response to the growing concerns regarding the security of individuals' health information in medical imaging. It is well known that in real-world hospit...
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Nowadays, numerous encryption schemes have been proposed in response to the growing concerns regarding the security of individuals' health information in medical imaging. It is well known that in real-world hospital scenarios, medical equipment generates a batch of images at one time, and doctors utilize them to diagnose patients' health conditions. Existing schemes have primarily focused on encrypting a single image;however, the lack of design for encrypting batch images leads to low flexibility in practical applications. To address this practical challenge, we propose a batch medical image encryption scheme. This scheme considers all the pixels in the batch images as a three-dimensional pixel cube and encrypts them using a Latin cube-based simultaneous permutation and diffusion technique to improve encryption efficiency. Through experimental results and security analysis, our scheme demonstrates strong key sensitivity and effectively resist various cryptographic attacks, such as brute-force attack, statistical attacks, and differential attack.
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