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This article presents a flexible and novel motion detection scheme over a smart device camera in OCC.
Proceedings ArticleDOI
20 Sep 2011
30 Citations
In conclusion, an effective motion assessment and monitoring system has been developed for the improvement of the motion detection ability.
Our results indicate that the later stage, in which motion integration and center-surround interaction appears, is critical for determining the perceptual limit of motion detection.
Proceedings ArticleDOI
Yu-Fei Ma, Hong-Jiang Zhang 
01 Aug 2001
49 Citations
Experimental results show that the proposed approach is effective for motion objects detection, and it can be used in real-time surveillance.
Proceedings ArticleDOI
Wu Huimin, Zheng Xiaoshi, Zhao Yanling, Li Na 
19 Dec 2008
12 Citations
Motion detection with this thresholding method is accurate and real-time.
Book ChapterDOI
Jaehyeok Han, Doowon Jeong, Sang Jin Lee 
06 Oct 2015
5 Citations
Thus, this paper comprehensively analyzes the HIKVISION DVR file system and proposes a reliable method for digital forensic analyses.
Open accessJournal ArticleDOI
13 Jul 2015-Optics Express
27 Citations
Experimental and simulation results demonstrate the validity of the proposed VLC based motion detection technique.
This paper proposes a simple and efficient surveillance system based on motion detection with motion vector estimation from surveillance video frames.
Proceedings ArticleDOI
20 Sep 2011
30 Citations
The result of this study is expected to be beneficial and able to assist users on effective motion detection and analysis.

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Oil palm tree detection using deep learning ?
5 answers
Detection of oil palm trees using deep learning techniques has shown promising results in various aspects. Studies have utilized deep learning models like convolutional neural networks to detect diseases such as Basal Stem Rot caused by Ganoderma Boninense. Additionally, deep learning methods have been employed to estimate the maturity level of oil palm fruit with high accuracy levels reaching up to 99%. Furthermore, the potential of deep learning in combination with data augmentation techniques has been highlighted for analyzing oil palm remote sensor data, showing significant potential in detecting diseases like basal stem rot on a commercial scale. Moreover, object detection techniques using deep learning architectures like Faster R-CNN have been proposed for automatically estimating the age of oil palm trees based on crown size, showcasing the versatility of deep learning in various aspects of oil palm tree management.
High-sope cues in IS?
5 answers
High-scope cues in Importance Sampling (IS) refer to utilizing visual features beyond traditional saliency maps to enhance compositional balance assessment. While IS commonly relies on saliency maps for evaluation, recent research suggests incorporating high-level visual elements like human-provided data and dominant vanishing point detection can significantly improve balance prediction accuracy. This approach aligns with the concept of leveraging human-understandable cues in Explainable AI (XAI) to enhance model interpretability and align with human intuition. By integrating mid-level and high-level visual cues, such as multi-scale segmentation, background priors, spatial color distribution, and objectness evaluation, a more comprehensive and effective saliency object detection method can be achieved, outperforming existing techniques. This holistic approach mirrors the human visual system's ability to combine appearance matching with higher-level reasoning for 3D scene reconstruction, emphasizing the importance of integrating various visual cues for accurate assessments.
What is webAR?
5 answers
WebAR, or Web-based Augmented Reality, is a technology that leverages the web browser to deliver augmented reality experiences without the need for specialized applications or installations. It combines AR with other technologies like IoT, hand-tracking, and lightweight neural networks to create innovative applications. WebAR systems can measure user immersion and engagement in educational settings, monitor air quality in smart environments, and enhance object detection accuracy in mobile AR applications. Additionally, WebAR enables the creation of VR experiences through WebXR, providing a platform for easy distribution and accessibility of VR content. Overall, WebAR offers a versatile and user-friendly approach to experiencing augmented reality through web browsers, making it widely accessible across various devices and platforms.
What are the most important papers by Florence Bareyre?
5 answers
Florence Bareyre's significant papers include those focusing on the seismic vulnerability assessment of modern masonry buildings in Florence, Italy. Additionally, Bareyre's works contribute to the analysis of territorial impacts of future projects in the area around Florence, starting from the province of Pistoia and extending to Florence through the Prato province. These papers shed light on the structural vulnerabilities of historical buildings in Florence and provide insights into the potential consequences of future development projects in the region. Bareyre's research plays a crucial role in understanding the architectural and seismic aspects of buildings in Florence, offering valuable information for urban planning and preservation efforts in the city.
What are the technologies for detecting crossheads and turns indoors with opencv technology?
4 answers
To detect crossheads and turns indoors using OpenCV technology, various methods have been proposed in recent research. One approach involves utilizing head detection and tracking algorithms like Haar-like features, Haar Training, and CMT. Another method introduces the Motion-aware Pseudo Siamese Network (MPSN), which leverages head motion information to guide deep models for effective head feature extraction in indoor scenarios. Additionally, a navigation system based on Visible Light Communication (VLC) using RGB LEDs and a photodetector has been developed for indoor applications, enabling spatial direction determination through optical signals. Furthermore, a real-time human head detection and tracking method using RGBD images has been proposed for fall detection systems, showcasing superior performance compared to existing methods. These technologies offer innovative solutions for accurate and efficient indoor head detection and tracking.
How effectiveness is police visibility in terms of loitering?
5 answers
Police visibility plays a crucial role in addressing loitering issues. The effectiveness of police visibility in dealing with loitering can be understood through the concept of rendering themselves selectively visible and invisible in the landscape. Additionally, the detection and tracing of moving objects in certain areas for extended periods, as well as the visualization of loitering behavior through trajectory analysis, contribute to enhancing police visibility and response to loitering incidents. Furthermore, the impact of police behaviors, performance, and relations on the community is essential for assessing the overall effectiveness of policing strategies in addressing loitering concerns. By strategically utilizing visibility and surveillance technologies, police can effectively detect and deter loitering activities, contributing to a safer and more secure public space.
What is visual perception?
5 answers
Visual perception encompasses the process of receiving, organizing, identifying, and interpreting visual information to understand the environment. It involves both sensory functions, such as taking in visual stimuli, and specific mental functions, like organizing and interpreting these stimuli. Visual perception plays a crucial role in various fields, including industrial applications, education, and cognitive development. Different models and technologies, such as deep learning and YOLO network, have been developed to enhance visual perception, enabling faster and more accurate object detection. Enhancing visual perception can lead to improvements in skills like hand-eye coordination, spatial relationships, and cognitive abilities, ultimately impacting tasks such as reading, writing, and mathematical skills.
What is creation of bFP?
5 answers
The creation of BFP (Blue Fluorescent Protein) involves converting Enhanced Green Fluorescent Protein (EGFP) into BFP using the CRISPR/Cas9 system. This conversion allows for the quantification of Homology-Directed Repair (HDR) and Non-Homologous End Joining (NHEJ) processes. The process includes introducing specific base substitutions to shift the fluorescence spectrum towards the blue range, enabling the differentiation between HDR (blue fluorescence) and NHEJ (loss of fluorescence). The study utilized guide RNA vectors to target Cas9 near the EGFP locus, along with different repair templates to achieve the conversion. The creation of BFP through this method provides a versatile assay for quantifying CRISPR/Cas9-mediated genome editing, offering a simple yet effective strategy for assessing gene editing outcomes.
Why would YOLO be favorable for lincese plate detection compared to other algorithms?
5 answers
YOLO (You Only Look Once) is favored for license plate detection due to its efficiency and accuracy. YOLO technology utilizes regression theory to swiftly detect objects with high precision, making it ideal for real-time applications like license plate recognition. YOLO models, such as YOLOv5-LC, offer smaller model sizes, faster inference speeds, and improved detection accuracy, making them suitable for deployment on various devices. Compared to other algorithms, YOLO excels in recognizing license plates accurately and promptly, enabling effective monitoring of traffic violations like missing or improper license plates. The ability of YOLO to efficiently process images, detect objects, and provide precise bounding boxes makes it a valuable tool for enhancing road safety through automated license plate detection.
How good is YOLO for object detection?
5 answers
YOLO (You Only Look Once) is a highly effective object detection algorithm that has shown significant advancements in the field of computer vision. Various versions of YOLO, such as YOLOv2, YOLOv5, and YOLO-Drone, have been proposed with improvements tailored to different applications. YOLO models have demonstrated superior detection accuracy, precision, recall, and Intersection over Union (IOU) metrics when compared to state-of-the-art detectors. Additionally, YOLO algorithms have been proven to outperform classical object detection methods in terms of detection performance, especially in scenarios involving small objects, remote sensing images, and UAV applications. Despite its strengths, YOLO algorithms may face challenges in handling noisy environments, which can impact their performance.
How good is YOLO for license plate detection?
5 answers
YOLO (You Only Look Once) has proven to be highly effective for license plate detection in various scenarios. Different versions of YOLO, such as YOLOv4 and YOLOv5, have been utilized to enhance license plate recognition accuracy. These models incorporate innovative techniques like attention modules, feature pyramid networks, and Transformer prediction heads to improve small target detection and recognition accuracy, even in challenging conditions like low-quality images or complex scenes. Additionally, the end-to-end optimization algorithms based on YOLOv3 have shown superior recognition abilities, outperforming commercial systems and demonstrating better stability in difficult datasets. Overall, YOLO-based approaches have achieved impressive accuracy rates ranging from 89% to 90.3% in license plate detection tasks, making them a reliable choice for such applications.