TransLink: Transformer-based Embedding for Tracklets' Global Link

Multi-object tracking (MOT) is essential to many tasks related to the smart transportation. Detecting and tracking humans on the road can give a vital feedback for either the moving vehicle or traffic control to ensure better driving safety and traffic flow. However, most trackers face a common problem of identity (ID) switch, resulting in an incomplete human trajectory prediction. In this paper, we propose a Transformer-based tracklet linking method called TransLink to mitigate the association failures. Specifically, the self-attention mechanism is well exploited to get the feature representation for tracklets, followed by a multilayer perceptron to predict the association likelihood, which can be further used in determining the tracklet association. Experiments on the MOT dataset demonstrate the effectiveness of the proposed module in lifting the tracking performances.