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Detection and Tracking on Automotive Radar Data with Deep Learning | IEEE Conference Publication | IEEE Xplore
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Detection and Tracking on Automotive Radar Data with Deep Learning


Abstract:

Reliable tracking of road users plays a critical part on the way to safe automated driving. In this paper, a machine learning based tracking approach on radar data is pre...Show More

Abstract:

Reliable tracking of road users plays a critical part on the way to safe automated driving. In this paper, a machine learning based tracking approach on radar data is presented utilizing the radar target point clouds from multiple time steps as input to detect road users and to predict their tracking information. The detection and tracking of objects is achieved by applying a combination of known feature extractors from lidar and camera detection tasks. The generated feature maps are used as input to two branches - one branch for detection and one for tracking. In experiments on an extensive real-world radar data set, the proposed model achieves promising results in tracking performance compared to a basic clustering and a classification assisted tracking approach.
Date of Conference: 06-09 July 2020
Date Added to IEEE Xplore: 10 September 2020
ISBN Information:
Conference Location: Rustenburg, South Africa

References

References is not available for this document.