Deep Recurrent Neural Network for Multi-target Filtering
This work addresses multi-target tracking for applications like surveillance or robotics, but appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of multi-target filtering with fixed motion and measurement models by using an adaptive learning framework with recurrent neural networks and a novel data association algorithm, achieving highly promising results in simulation.
This paper addresses the problem of fixed motion and measurement models for multi-target filtering using an adaptive learning framework. This is performed by defining target tuples with random finite set terminology and utilisation of recurrent neural networks with a long short-term memory architecture. A novel data association algorithm compatible with the predicted tracklet tuples is proposed, enabling the update of occluded targets, in addition to assigning birth, survival and death of targets. The algorithm is evaluated over a commonly used filtering simulation scenario, with highly promising results.