Segment as Points for Efficient and Effective Online Multi-Object Tracking and Segmentation.

IEEE Transactions on Pattern Analysis and Machine Intelligence
Zhenbo XuLiusheng Huang

Abstract

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for tracking. However, due to the large receptive field of deep convolutional neural networks, the foreground areas of the current instance and the surrounding areas containing nearby instances or environments are usually mixed up in the learned instance embeddings, resulting in ambiguities in tracking. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation and learning instance embedding in a point cloud processing manner. Moreover, multiple informative data modalities are formulated as point-wise representations to enrich point-wise features. In addition, to enable the practical utility of MOTS, we modify the one-stage method SpatialEmbedding for instance segmentation. The resulting efficient and effective framework, named PointTrackV2, outperforms all the state-of-the-art methods including 3D tracking methods by large margins with the near real-time speed. Extensive evaluations on three datasets demonstrate both the effectiv...Continue Reading

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