论文标题
SQE：多对象跟踪中参数优化的自质量评估度量
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
论文作者
论文摘要
我们提出了一种新颖的自我质量评估公式SQE，以在具有挑战性但至关重要的多目标跟踪任务中优化参数。当前的评估指标都需要注释的地面真理，因此在测试环境和现实情况下将失败，禁止训练后进一步优化。相比之下，我们的度量反映了轨迹假设的内部特征，并衡量没有地面真理的跟踪性能的措施。我们证明，具有不同质量的轨迹在特征距离分布上表现出不同的单个或多个峰，激发了我们设计一种简单而有效的方法，可以使用两级高斯混合物模型评估轨迹的质量。在MOT16挑战数据集上进行的实验验证了我们方法在与现有指标相关的相关性和启用参数自我优化以实现更好的性能的有效性。我们认为，我们的结论和方法激发了实践中未来的多对象跟踪。
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task. Current evaluation metrics all require annotated ground truth, thus will fail in the test environment and realistic circumstances prohibiting further optimization after training. By contrast, our metric reflects the internal characteristics of trajectory hypotheses and measures tracking performance without ground truth. We demonstrate that trajectories with different qualities exhibit different single or multiple peaks over feature distance distribution, inspiring us to design a simple yet effective method to assess the quality of trajectories using a two-class Gaussian mixture model. Experiments mainly on MOT16 Challenge data sets verify the effectiveness of our method in both correlating with existing metrics and enabling parameters self-optimization to achieve better performance. We believe that our conclusions and method are inspiring for future multi-object tracking in practice.
