A multiple object tracking method using Kalman filter

TLDR

Maintaining target identity in multi‑object tracking is crucial for applications such as behavior analysis, yet real‑time challenges like occlusion, background interference, splits, and merges often degrade performance. The paper proposes a feature‑based Kalman‑filter algorithm to track multiple objects. The fully automatic system employs a Kalman‑filter motion model built on centroid and area features, uses detection‑derived cost functions to resolve correspondence after splits, and requires no manual initialization. Experiments on human and vehicle sequences demonstrate efficient tracking of multiple moving objects even in confusing scenarios.

Abstract

It is important to maintain the identity of multiple targets while tracking them in some applications such as behavior understanding. However, unsatisfying tracking results may be produced due to different real-time conditions. These conditions include: inter-object occlusion, occlusion of the ocjects by background obstacles, splits and merges, which are observed when objects are being tracked in real-time. In this paper, an algorithm of feature-based using Kalman filter motion to handle multiple objects tracking is proposed. The system is fully automatic and requires no manual input of any kind for initialization of tracking. Through establishing Kalman filter motion model with the features centroid and area of moving objects in a single fixed camera monitoring scene, using information obtained by detection to judge whether merge or split occurred, the calculation of the cost function can be used to solve the problems of correspondence after split happened. The algorithm proposed is validated on human and vehicle image sequence. The results shows that the algorithm proposed achieves efficient tracking of multiple moving objects under the confusing situations.