3D tracking targets via kinematic model weighted particle filter

Automatically and reliably tracking numerous flying objects in 3D space is of great significance for not only scientific researches such as collective behavior analysis, but also practical applications such as designing multi-agent robots. However, it remains a challenging task due to the large population, similar appearance, and severe occlusion happening in 2D images. This paper proposes a 3D tracking method that is capable of tracking individuals of a swarm of flying objects using the particle filtering technique. Each particle is not only weighted by the observation model but also weighted by the kinematic model. The kinematic model is modeled by learning a long short-term memory network on sequences of velocities. Experimental results show that the kinematic model significantly improves the efficiency of estimating target's motion state, and show that the proposed method outperforms the state-of-the-art methods.