Performance Study of Multi-target Tracking Using Kalman Filter and Hungarian Algorithm

Abstract

We present the method for multi-target tracking using the combination of Kalman filter and Hungarian algorithm and test the efficiency of this method with two different data sets. In Data set – I, no target leave or enter the frame and in Data set – II, targets leave and enter the frame at regular intervals. This tracking method deals with the data association problem that arises with multiple targets in a single frame and also the dimensionality problem that arises due to repeated changes in the size of state-space associated with multiple targets. We use 2 important methods to achieve this. The first is the Kalman filter which is an extension of Bayesian filter. It uses a probabilistic approach to deal with the estimation of data. The second one is the Hungarian algorithm, used to overcome the data association problem and data association comes into the picture only when there are multiple targets.