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Assisting Autonomous vehicles with Traffic Video Dataset

Introduction

• Datasets for Machine Learning in Autonomous Vehicles
• Datasets with multiple sensor modalities (LiDAR, RADAR, Stereo Camera, Thermal Camera etc.)

A wide variety of sensors are used in autonomous vehicles. The diversity of sensing modalities helps in different weather conditions. The following is a popular list of autonomous driving datasets which have been published up to date.

How Video Annotation Supports Autonomous Vehicles:

Autonomous vehicle technology promises to make our roads safer, whilst increasing the efficiency of transport and delivery services. Safety and reliability are the key factors necessary for the widespread adoption of self-driving vehicles. The algorithms powering cars, trucks, and buses on our roads are required to function perfectly in all circumstances.

In order to navigate obstacles and operate effectively in chaotic real world traffic conditions, computer vision based models must be trained with annotated data that adds information and labels to images and Traffic Video Dataset. For automated vehicles to reach their potential machine learning developers need access to precise training data at large scales. This blog will focus on the video annotation and show how it is contributing to the development of autonomous vehicles. Collaborating with the video annotation specialists, like Keymakr, is allowing pioneers in this sector to continue the growth of this essential technology.

Video Annotation

Video Annotation allows information to be added to video data. Annotation tools are  used to label objects of interest, or segment pixels in target classes, in each frame of video. Due to the time consuming nature of annotating thousands of frames per video, techniques such as object interpolation are often utilized.

This annotation feature can track and locate objects through multiple frames automatically, increasing the speed and efficiency of the labeling process. Labeling conventions and practices are outlined by machine learning engineers, and communicated to the annotators who then create the training datasets.

Self-driving Vehicles

Video annotation creates data that replicates the complex movements and interactions present in real world environments. Models trained with effective video data can operate in chaotic traffic conditions, making use of the following capabilities.

OBJECT DETECTION:

Object detection allows autonomous vehicles to identify and respond to specific objects. During Video Annotation bounding boxes are placed around objects, which are then assigned a label e.g. car, bus, etc. Through exposure to training video annotated in this way computer vision models are able to recognize and navigate around important road objects.

OBJECT CLASSIFICATOIN:

 Semantic segmentation helps to contextualize and add detail to video training data. Annotators use annotation tools to assign every pixel in each frame to a particular class, e.g. road, sidewalk, sky. This additional granularity helps self-driving vehicles to travel with precision.

LANE RECOGNITION:

In order to be deployed safely on public roads automated vehicle models must be able to recognize lane markings and stay within them. To achieve this video training data is annotated with polylines. These lines can define the parallel shapes of lanes, allowing autonomous vehicles to stay within safe limits.

CONCLUSION

Autonomous vehicles will benefit the economy through fuel efficiency, the environment through reduced carbon emissions, society through more togetherness, and the legal system through a simpler system of liability.

Outsourcing Traffic Video Dataset with GTS.AI

GTS.AI is a platform that provides traffic outsourcing services to website owners, advertisers, and marketers. Traffic outsourcing involves hiring a third-party service provider to generate traffic and other Dataset For Machine Learning to a website or online business. By outsourcing traffic to GTS.AI, website owners and businesses can save time and resources that would otherwise be spent on marketing and advertising. They can also benefit from GTS.AI's expertise and experience in traffic generation, which can help them to achieve better results and reach a wider audience.