3D Multi-Object Tracking: A Baseline and New Evaluation Metrics

9 Jul 2019Xinshuo WengJianren WangDavid HeldKris Kitani

3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as computational cost and system complexity... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
3D Multi-Object Tracking KITTI 3D Kalman Filter + Birth and Death Memory MOTA 83.34% # 2
MOTP 85.23% # 2

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet