{"id":19865,"date":"2019-11-11T15:15:09","date_gmt":"2019-11-11T14:15:09","guid":{"rendered":"https:\/\/www.intellias.com\/?p=19865"},"modified":"2023-08-21T09:33:48","modified_gmt":"2023-08-21T07:33:48","slug":"how-to-apply-kalman-filter-for-pedestrian-recognition","status":"publish","type":"blog","link":"https:\/\/intellias.com\/how-to-apply-kalman-filter-for-pedestrian-recognition\/","title":{"rendered":"How to Apply Kalman Filter for Pedestrian Recognition"},"content":{"rendered":"

Intelligent, self-guided, and self-driving vehicle systems are closer than you think. They may not be roaming the streets just yet, but the pace of technological advancement is pushing the advent of self-driving cars closer. The automatic system utilizes computer vision algorithms to detect and predict activity for all other traffic participants, including other vehicles, pedestrian recognition or even wild animals.<\/p>\n

For a self-driving car, pedestrian detection is a vital feature to ensure safety. So, the main goal of the machine learning approach in the automotive domain is the possibility to predict dangerous situations on the road ahead of time. A critical benefit the system provides to the cars is distinguishing pedestrians and vehicles in motion so that they make smarter and safer driving decisions.<\/p>\n

In this article, we go through the machine learning development services<\/a> and methodologies used in autonomous vehicles, such as object detection, object segmentation and prediction to track their surroundings properly, pedestrian detection, predict object locations and possible collisions. We do not only explain how real-time pedestrian detection works in theory but also display the practical implementation of the methodologies in self-driving cars:
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