3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection. For occlusion problems, we are the first to propose an occlusion head to select the best object appearance features multiple times effectively, reducing the influence of occlusions. To decrease the impact of false detection in tracking, we design a motion cost matrix based on confidence scores which improve the positioning and object prediction accuracy in 3D space. As existing multi-object tracking methods only consider a single category, we also propose to build a multi-category loss to implement multi-object tracking in multi-category scenes. A series of validation experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our proposed method achieves state-of-the-art performance and the lowest identity switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal MOT methods on the KITTI test dataset. And our proposed method achieves state-of-the-art performance among all algorithms on the nuScenes test dataset with 75.3% AMOTA.
翻译:3D 多球跟踪(MOT) 确保连续动态检测的一致性,有利于自动驾驶的连续运动规划和导航任务。然而,以相机为基础的方法在闭塞方面会受到影响,准确跟踪基于激光雷达(LiDAR)方法的物体的不规则移动可能具有挑战性。有些聚合方法效果良好,但并不认为在闭塞下出现不可信的外观特征问题。与此同时,错误检测问题也会显著影响跟踪。因此,我们提议基于复合外观-运动3 优化(CAMO-MOT) 的新相机-LiDAR 混合3D MOT框架。使用相机和激光雷达(LiDAR) 数据来准确跟踪由闭塞和假检测导致的物体运动。对于闭塞问题,我们首先提出一个隐蔽头来选择最好的物体外观特征,从而减少所有隐蔽的影响。为了减少在跟踪中进行虚假检测的影响,我们设计一个基于信任度评分的移动成本矩阵,提高3D空间的定位和对象预测准确性能。 现有的多轨道测试S-road 数据跟踪方法也用于我们多轨道的多轨道。