Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Online multi-object tracking with a single moving camera is a challenging problem as the assumptions of 2D conventional motion models (e.g., first or second order models) in the image coordinate no longer hold because of global camera motion. In this paper, we consider motion context from multiple objects which describes the relative movement between objects and construct a Relative Motion Network (RMN) to factor out the effects of unexpected camera motion for robust tracking. The RMN consists of multiple relative motion models that describe spatial relations between objects, thereby facilitating robust prediction and data association for accurate tracking under arbitrary camera movements. The RMN can be incorporated into various multi-object tracking frameworks and we demonstrate its effectiveness with one tracking framework based on a Bayesian filter. Experiments on benchmark datasets show that online multi-object tracking performance can be better achieved by the proposed method.

1. Introduction

Multi-object tracking (MOT) is of great importance for numerous computer vision tasks with applications such as surveillance, traffic safety, automotive driver assistance systems, and robotics. Thanks to advances of object detectors [3], [4], detection-based MOT methods have been extensively studied in recent years. In this approach, the goal is to determine the trajectories and identities of target instances throughout an image sequence using the detection results of each frame as observations.