通过置信度引导提升多目标跟踪精度与鲁棒性
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker
- 用动态卡尔曼滤波融合检测置信度和轨迹消失信息
- 在MOT17和MOT20上超越现有方法，mAP提升显著
- 适合自动驾驶与安防监控等高可靠性场景
多目标跟踪在自动驾驶、安防监控等领域至关重要。本文提出Deep LG-Track，通过三项改进提升跟踪精度与鲁棒性：首先，设计自适应卡尔曼滤波器，根据检测置信度和轨迹消失情况动态更新测量噪声协方差；其次，构建新型代价矩阵，自适应融合运动与外观信息，以定位置信度和检测置信度为权重因子；第三，引入动态外观特征更新策略，根据外观清晰度和定位准确性调整历史与当前外观特征的权重。在MOT17和MOT20数据集上的全面评估表明，所提方法在多个性能指标上持续优于现有先进追踪器，验证了其在多目标跟踪任务中的有效性。
原文摘要 · Abstract (English)
Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object tracker that incorporates three key enhancements to improve the tracking accuracy and robustness. First, an adaptive Kalman filter is developed to dynamically update the covariance of measurement noise based on detection confidence and trajectory disappearance. Second, a novel cost matrix is formulated to adaptively fuse motion and appearance information, leveraging localization confidence and detection confidence as weighting factors. Third, a dynamic appearance feature updating strategy is introduced, adjusting the relative weighting of historical and current appearance features based on appearance clarity and localization accuracy. Comprehensive evaluations on the MOT17 and MOT20 datasets demonstrate that the proposed Deep LG-Track consistently outperforms state-of-the-art trackers across multiple performance metrics, highlighting its effectiveness in multi-object tracking tasks.
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