Automatic Class Identification and Motion Classification for Improved Multitarget Tracking

Abstract

<p>Target classification has received significant attention in tracking literature. Algorithms for joint tracking and classification that are capable of improving tracking performance by exploiting the inter-dependency between target class and target kinematic behavior have already been proposed. However, in previous works the possible types of classes were assumed to be known a prior and the problem of class identification itself was not considered. In practice, the prior class information may not be always available. In this thesis, motivated by a people tracking problem, a joint class identification and target classification algorithm that can simultaneously build class types on the basis of target kinematic and feature measurements and classify targets according to the identified classes even when there is switching among classes is proposed. In addition, a new concept called "class quality" is introduced to improve the class identification and target classification accuracy. Accordingly, a modified performance evaluation metric for multiple object estimation, called Quality-based Optimal Subpattern Assignment (Q-OSPA), is proposed to quantify the class identification performance of the proposed algorithm. This metric provides more intuitively appealing results than the original OSPA metric when the quality of estimates is available. This new metric is also applicable in standard tracking problems where classification or class identification is not carried out, but a track quality measure is available as in the case of the Mnltiple Hypothesis Tracking (MHT) or the Joint Integrated Probabilistic Data Association (JIPDA) algorithm. Besides theoretical derivations, extensive simulations are presented to verify the effectiveness of the proposed algorithm.</p>