Classification of Tracked Objects Using Multiple Frame Processing for Automotive Radar
Mujtaba Hassan (Microwave Sensing, Signals & Systems, Nxp Semiconductors)
Francesco Fioranelli (Microwave Sensing, Signals & Systems)
Alexander Yarovoy (Microwave Sensing, Signals & Systems)
Lihui Chen (NXP Semiconductors)
Satish Ravindranath (NXP Semiconductors)
Ryan Wu (NXP Semiconductors)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
A neural network (NN) based multi-frame classification approach is proposed to solve the problem of classification of tracked objects. Initially, a baseline tracker is implemented that uses the classification output of an object detection network for classification. Afterwards, two approaches for multi-frame classification are applied to perform classification of tracked objects. The first approach aggregates points from multiple frames and applies a single frame NN for classification, whereas the second approach uses bidirectional long short term memory (BiLSTM) layers to process points from multiple frames. Extensive experiments on the opensource 2D RadarScenes dataset showed a consistent increase in track performance when using either of the two techniques for multi-frame classification.