Abstract
The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (~400 m) is addressed; the interacting multiple model (IMM) estimator combined with the directional track-to-track association (abbreviated as track association) is proposed. The previous work of the Kalman estimator with the track association approach is extended to the IMM estimator with the directional track association. The IMM estimator can handle multiple targets with various maneuvers. The track association scheme is modified in consideration of the direction of the target movement. The overall system is composed of moving object detection for measurement generation and multiple target tracking for state estimation. The moving object detection consists of frame-to-frame subtraction of three-color layers and thresholding, morphological operation, and false alarm removing based on the object size and shape properties. The centroid of the detected object is input into the next tracking stage. The track is initialized using the difference between two nearest points measured in consecutive frames. The measurement nearest to the state prediction is used to update the state of the target for measurement-to-track association. The directional track association tests both the hypothesis and the maximum deviation between the displacement and directions of two tracks followed by track selection, fusion, and termination. In the experiment, a multirotor flying at an altitude of 400 m captured 55 moving vehicles around a highway interchange for about 20 s. The tracking performance is evaluated for the IMMs using constant velocity (CV) and constant acceleration (CA) motion models. The IMM-CA with the directional track association scheme outperforms other methods with an average total track life of 91.7% and an average mean track life of 84.2%.
Highlights
Small unmanned aerial vehicles (UAVs) or drones are very useful for many applications such as security and surveillance, search and rescue mission, traffic monitoring, and asset and environmental inspection [1,2]
Since the initial acceleration cannot be obtained with two points, it is set to zero in xitni(k − 1|k − 1), the target is assumed to move at a constant velocity
The interacting multiple model (IMM)-constant acceleration (CA) scheme was compared with the IMM-constant velocity (CV) when applying track association or directional track association
Summary
Small unmanned aerial vehicles (UAVs) or drones are very useful for many applications such as security and surveillance, search and rescue mission, traffic monitoring, and asset and environmental inspection [1,2]. An IMM-directional track association scheme is proposed for tracking multiple targets captured by a multirotor. The strategy developed in [19,20,21] extracts measurements during the object detection, and establishes the tracks based on those measurements This method does not directly use the intensity information for target tracking, there is no need to store or transmit high-resolution video streams. The nearest neighbor (NN) measurement–to–track association is the most effective in computing and has been successfully applied to target tracking based on multiple frames captured by a drone [19,20,21,22]. The track fusion method in multi-sensor environments was developed assuming a common process noise of AAppppl.l.SSccii..22002211,,1111,,1x12F3O4R PEER REVtIEhWe target [18].
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