Robust and fast detection of moving vehicles in aerial videos using sliding windows
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Robust and Fast Detection of Moving Vehicles in Aerial Videos using Sliding Windows proposes a method to detect moving vehicles in aerial videos using a combination of track-before-detect and machine learning. An AdaBoost classifier is trained on low resolution vehicle appearances and applied within a sliding window algorithm to detect vehicles in regions of interest identified by track-before-detect. The paper contributes by identifying, optimizing, and evaluating the most important parameters to achieve both high detection rates and real-time processing of aerial videos.
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Deep learning techniques are being used for obstacle detection and avoidance in driverless cars. A convolutional neural network model is trained to analyze video/images in real-time from an IoT device like a Raspberry Pi that controls the car. The CNN model can detect obstacles and allow the car to avoid collisions. It was tested and achieved 88.6% accuracy in detecting obstacles, demonstrating it can successfully support autonomous driving capabilities.
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This document proposes and tests a deep learning-based system for real-time traffic volume counting on high-traffic urban arterial roads. Video clips from 4 camera views along arterial roads with estimated annual average daily traffic over 50,000 vehicles were used to test the system. The system achieved average accuracy rates between 93.84-97.68% across the camera views for 5 and 15-minute video clips. It was also able to process frames in real-time at an average of 37.27ms per frame. The proposed system provides an accurate and efficient method for traffic authorities to conduct traffic volume surveys on busy urban roads.
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Real Time Object Identification for Intelligent Video Surveillance Applications
Intelligent video surveillance system has emerged as a very important research topic in the computer vision field in the
recent years. It is well suited for a broad range of applications such as to monitor activities at traffic intersections for detecting
congestions and predict the traffic flow. Object classification in the field of video surveillance is a key component of smart
surveillance software. Two robust methodology and algorithms adopted for people and object classification for automated surveillance
systems is proposed in this paper. First method uses background subtraction model for detecting the object motion. The background
subtraction and image segmentation based on morphological transformation for tracking and object classification on highways is
proposed. This algorithm uses erosion followed by dilation on various frames. Proposed algorithm in first method, segments the image
by preserving important edges which improves the adaptive background mixture model and makes the system learn faster and more
accurately. The system used in second method adopts the object detection method without background subtraction because of the static
object detection. Segmentation is done by the bounding box registration technique. Then the classification is done with the multiclass
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AI in mechanical engineering.pptx
The document discusses artificial intelligence in automobiles and autonomous vehicles. It describes how autonomous cars use technologies like GPS, LIDAR, radar, video cameras, and position estimators to drive themselves without human assistance. The components work together to create a precise 3D map of the vehicle's surroundings and determine the path, speed, and direction. While autonomous vehicles promise advantages like reduced accidents and traffic, challenges remain around issues of hacking and sensor failures.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
Low Cost, Effective Solution for Monitoring, Controlling and Tracking of Rent...
This document proposes a low-cost solution for monitoring, controlling, and tracking rented vehicles used in a vehicle sharing system. The system uses technologies like GPS, GSM, and a face detection system (FDS) to provide features such as user authentication, theft detection, collision detection, distance tracking, and remote vehicle control. A prototype was created using a microcontroller, GPS/GSM modules, sensors, and other hardware. It was able to successfully demonstrate the anti-fraud, monitoring, and control capabilities of the proposed vehicle sharing system.
Satish prasad_presentation on hybrid cars
The document discusses the features and technologies of hybrid cars. Hybrid cars can use vision systems to detect other vehicles, traffic signals, pedestrians and obstacles. They can also monitor the driver's physiology, road and weather conditions. Key features of hybrid cars mentioned include collision avoidance, adaptive cruise control, imaging technologies and navigational aids that provide safety and convenience. The document outlines some challenges in further developing and adopting intelligent vehicle systems and envisions future vehicles having improved in-vehicle systems and communication abilities.
Satish prasad bangalore india_satmd410
The document discusses the features and technologies of hybrid cars. Hybrid cars can use vision systems to detect other vehicles, traffic signals, pedestrians and obstacles. They can also monitor the driver's physiology, road and weather conditions. Key features include collision avoidance systems, adaptive cruise control, imaging technologies and navigation aids to improve safety. The hybrid cars of the future are expected to have more advanced autonomous capabilities and communication between vehicles and infrastructure to further reduce accidents.
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...Associate Professor in VSB Coimbatore
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.Ieeepro techno solutions ieee 2013 embedded project gravity lock next gen...
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Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
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Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
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Robust and fast detection of moving vehicles in aerial videos using sliding windows
- 1. 2020 – 2021 #13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, Vellore – 6. Off: 0416-2247353 Mo: +91 9500218218 / +91 8220150373 Website: www.shakastech.com, Email - id: shakastech@gmail.com, info@shakastech.com Robust and Fast Detection of Moving Vehicles in Aerial Videos using Sliding Windows Abstract The detection of vehicles driving on busy urban streets in videos acquired by airborne cameras is challenging due to the large distance between camera and vehicles, simultaneous vehicle and camera motion, shadows, or low contrast due to weak illumination. However, it is an important processing step for applications such as automatic traffic monitoring, detection of abnormal behaviour, border protection, or surveillance of restricted areas. In contrast to commonly applied object segmentation methods based on background subtraction or frame differencing, we detect moving vehicles using the combination of a track-before-detect (TBD) approach and machine learning: an AdaBoost classifier learns the appearance of vehicles in low resolution and is applied within a sliding window algorithm to detect vehicles inside a region of interest determined by the TBD approach. Our main contribution lies in the identification, optimization, and evaluation of the most important parameters to achieve both high detection rates and real-time processing.