Online CNN-based multiple object tracking with enhanced model updates and identity association

Abstract

Online multiple objects tracking (MOT) is a challenging problem due to occlusions and interactions among targets. An online MOT method with enhanced model updates and identity association is presented to handle the error drift and the identity switch problems in this work. The proposed MOT system consists of multiple single CNN(Convolutional Neural Networks)-based object trackers, where the shared CONV layers are fixed and used to extract the appearance representation while target-specific FC layers are updated online to distinguish the target from background. Two model updates are developed to build an accurate tracker. When a target is visible and with smooth movement, we perform the incremental update based on its recent appearance. When a target experiences error drifting due to occlusion, we conduct the refresh update to clear all previous memory of the target. Moreover, we introduce an enhanced online ID assignment scheme based on multi-level features to confirm the trajectory of each target. Experimental results demonstrate that the proposed online MOT method outperforms other existing online methods against the MOT17 and MOT16 benchmark datasets and achieves the best performance in terms of ID association.