JLA

2000년 도입 · 논문 1편에서 사용

JLA, or Joint Learning Architecture, is an approach for multiple object tracking and trajectory forecasting. It jointly trains a tracking and trajectory forecasting model, and the trajectory forecasts are used for short-term motion estimates in lieu of linear motion prediction methods such as the Kalman filter. It uses a FairMOT model as the base model because this architecture already performs detection and tracking. A forecasting branch is added to the network and is trained end-to-end. FairMOT consist of a backbone network utilizing Deep Layer Aggregation, an object detection head, and a reID head.

출처: Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting

소개 논문: Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting