Unsupervised Auto-Encoding Multiple-Object Tracker for Constraint-Consistent Combinatorial Problem

Abstract

Multiple-object tracking (MOT) and classification are core technologies for processing moving point clouds in radar or lidar applications. For accurate object classification, the one-to-one association relationship between the model of each objects’ motion (trackers) and the observation sequences including auxiliary features (e.g., radar cross section) is important. In this work, we propose an unsupervised neural MOT model for accurate semi-automatic association labeling and we tackle the challenging one-to-one constrained combinatorial association problem by applying relaxation techniques. Experimental results demonstrate that our neural MOT model generates a more constraint-consistent association solution than conventional row-wise softmax methods.