S3MOT: Monocular 3D Object Tracking with Selective State Space Model

• URL: http://arxiv.org/abs/2504.18068v1
• Date: Fri, 25 Apr 2025 04:45:35 GMT
• Title: S3MOT: Monocular 3D Object Tracking with Selective State Space Model
• Authors: Zhuohao Yan, Shaoquan Feng, Xingxing Li, Yuxuan Zhou, Chunxi Xia, Shengyu Li,
• Abstract summary: Multi-object tracking in 3D space is essential for advancing robotics and computer applications.<n>It remains a significant challenge in monocular setups due to the difficulty of mining 3D associations from 2D video streams.<n>We present three innovative techniques to enhance the fusion of heterogeneous cues for monocular 3D MOT.
• Score: 3.5047603107971397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and reliable multi-object tracking (MOT) in 3D space is essential for advancing robotics and computer vision applications. However, it remains a significant challenge in monocular setups due to the difficulty of mining 3D spatiotemporal associations from 2D video streams. In this work, we present three innovative techniques to enhance the fusion and exploitation of heterogeneous cues for monocular 3D MOT: (1) we introduce the Hungarian State Space Model (HSSM), a novel data association mechanism that compresses contextual tracking cues across multiple paths, enabling efficient and comprehensive assignment decisions with linear complexity. HSSM features a global receptive field and dynamic weights, in contrast to traditional linear assignment algorithms that rely on hand-crafted association costs. (2) We propose Fully Convolutional One-stage Embedding (FCOE), which eliminates ROI pooling by directly using dense feature maps for contrastive learning, thus improving object re-identification accuracy under challenging conditions such as varying viewpoints and lighting. (3) We enhance 6-DoF pose estimation through VeloSSM, an encoder-decoder architecture that models temporal dependencies in velocity to capture motion dynamics, overcoming the limitations of frame-based 3D inference. Experiments on the KITTI public test benchmark demonstrate the effectiveness of our method, achieving a new state-of-the-art performance of 76.86~HOTA at 31~FPS. Our approach outperforms the previous best by significant margins of +2.63~HOTA and +3.62~AssA, showcasing its robustness and efficiency for monocular 3D MOT tasks. The code and models are available at https://github.com/bytepioneerX/s3mot.

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