Method and apparatus for the tracking of multiple objects

摘要

Tracking systems using multi-object-tracking (MOT) combine data from a plurality of sensors (101) to derive a state estimate of one or more objects (103) are described. The interfaces to the sensors (101) are such that different ones of the sensors (101) can be used. The tracking (102) in the system consists of different blocks for state estimation (104), object's existence estimation (106), and data association (105). To utilize the data of a sensor, the sensor data and the system which is observed is modeled using the Bayesian filtering framework. All interfaces of the system, i.e. the interfaces between the sensors (101) and the MOT system as well as of the blocks in the MOT are of a characteristic that ensures that only compatible models are used with each other already when designing the system.

主权项

1. A system for tracking one or more movable objects comprising:
one or more sensors recording object measurement data and passing the recorded object measurement data to a memory; a measurement space located in the memory for storing a plurality of measurement vectors representative of the recorded object measurement data, wherein the measurement space has a plurality of dimensions, the dimensions having a dimension identifier; a state space located in the memory for storing a plurality of state vectors representative of the state of the object, wherein the state space has a plurality of dimensions, the dimensions having a dimension identifier; a measurement noise space located in the memory for storing a plurality of measurement noise vectors representative of uncertainties in the object measurement data; a system model having a system deterministic part and a system probabilistic part and being stored in the memory, the system model being representative of the one or more movable objects; a measurement model having a measurement deterministic part and a measurement probabilistic part and being stored in memory; and a processor configured to update the system model based on a previous system model and using at least an updated one of the measurement vectors, the measurement noise vectors or the state vectors.