Object Detection in Tensor Decomposition Based Multi Target Tracking

Abstract

Non-linear filtering arises in many sensor applications such as for instance robotics, military reconnaissance, advanced driver assistance systems and other safety and security data processing algorithms. Since a closed-form of the Bayesian estimation approach is intractable in general, approximative methods have to be applied. Kalman or particle based approaches have the drawback of either a Gaussian approximation or a curse of dimensionality which both leads to a reduction in the performance in challenging scenarios. An approach to overcome this situation is state estimation using decomposed tensors. In this paper the Sequential Likelihood Ratio Test (SLRT) for object detection in tensor decomposition based target tracking is presented. The scheme closely follows the well-known and often applied approach of the track-oriented MHT.