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ADAS

Life-saving algorithms which secure road safety with highest accuracy
Radars for autonomous vehicles

Sensors for ADAS Technology: 3. RADARs

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

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Autonomous self-driving car showing Lidar

ADAS sensors: 2. LiDARs

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

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RGB Cameras

ADAS sensors: 1. RGB cameras

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

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Traffic lanes detection

Lanes Detection System

As a potentially life-saving solution, even a simple traffic lanes detection system requires the highest accuracy. For that reason, RSIP Vision‘s deep learning experts have developed an ADAS system which solves this challenge. Our specialized team is fully proficient in both image processing techniques and the autonomous vehicles environment. The use of perspective and the calculation of vanishing points are part of the solution that we develop and provide.

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Technologies integrated in ADAS

ADAS Future Opportunities and Challenges

Both the United States and the European Union have issued rulings mandating that by year 2020 all vehicles must be equipped with autonomous emergency-braking systems

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Vehicles on the road

Vehicle Localization with Lane Tracking

More than 1 million people killed and 50 million injured on the roads every year make driving safety a subject of the highest importance. ADAS technologies are at the forefront of this fight: a further proof of that is our car detection and localization solution enabling to detect a danger, create an alert, avoiding the obstacle or – in the worst case – minimizing damage. This system based on machine learning follows the highest quality standards and can save lives on the road.

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Driver and pedestrians at a crosswalk

Pedestrian Detection with Machine Learning

One of the most challenging tasks of ADAS operating in urban or rural environment is the detection of pedestrians. When human behavior can sometimes be unpredictable, ADAS systems can be programmed to track pedestrians and predict with high levels of accuracy their orientation and intentions: human lives are at stake and our software is key to save them. RSIP Vision‘s algorithms do not save lives only in medical applications, but also in automotive safety systems!

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Road Signs Detection - ADAS

Road signs detection with machine learning

Frequent variations of speed limits (mainly due to roadwork and other maintenance), offer ADAS technology a chance to help drivers respect traffic laws and security. The traffic signs detection software developed by RSIP Vision detects a road sign at distance and verifies via machine learning if it is a speed limit sign and what that limit is. These detection and classification processes being based on machine learning, our application is able to do things that regular “engineered” software cannot do.

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Radars for autonomous vehicles

Sensors for ADAS Technology: 3. RADARs

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

Read More
Autonomous self-driving car showing Lidar

ADAS sensors: 2. LiDARs

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

Read More
RGB Cameras

ADAS sensors: 1. RGB cameras

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

Read More
Agricultural Yield Prediction on tablet

Agricultural yield prediction using Deep Learning

Technology breakthrough and availability of new datasets are changing forever the world of agriculture. Software solutions using Deep Learning makes it possible for farm managers to produce accurate yield estimates on a simple smartphone or tablet. This precision agriculture solution is made available by pioneering software from RSIP Vision.

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Industrial intrusion detection

Intrusion Detection with Deep Learning

Detecting physical and virtual intrusions is a key process in ensuring information and property security. Physical intrusion detection refers to all attempts at break-ins to

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Generative Adversarial Networks at work

GAN for non-rigid object tracking

Object identification and tracking remains a challenging task in computer vision, despite advances in hardware, computational, and algorithmic developments. Difficulties arise, in part, due to

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object tracking in video frames

Object Tracking at High fps

Object tracking in video sequences is a classical challenge in computer vision, which finds applications in nearly all domains of the industry: from assembly line

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high resolution with CNN

High Resolution Image Reconstruction

Recovering a high-resolution (HR) image from a low resolution one is a classical problem in computer vision for which many algorithms have been developed to

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The algorithm searches for information in each frame

Temporal point process sampling in video

Object identification and tracking in a sequence of frames (video) consists of sampling of the scene, by e.g raster or uniform scatter, to extract features

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3D models from depth cameras

RGB-D SLAM building 3D models from depth cameras

In the past few years, depth cameras became common and easy to get. Several product are available in the market at a reasonable price, e.g.

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