IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter

• URL: http://arxiv.org/abs/2502.09672v1
• Date: Thu, 13 Feb 2025 01:55:32 GMT
• Title: IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
• Authors: Xiaohong Liu, Xulong Zhao, Gang Liu, Zili Wu, Tao Wang, Lei Meng, Yuhan Wang,
• Abstract summary: 3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects.<n>Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object.<n>We introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects.
• Score: 10.669576499007139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object throughout its entire tracking process. However, objects may change their motion patterns due to variations in the surrounding environment. In this paper, we introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects, overcoming the limitation of single-model tracking in existing approaches. In addition, we incorporate a Damping Window mechanism into the trajectory lifecycle management, leveraging the continuous association status of trajectories to control their creation and termination, reducing the occurrence of overlooked low-confidence true targets. Furthermore, we propose the Distance-Based Score Enhancement module, which enhances the differentiation between false positives and true positives by adjusting detection scores, thereby improving the effectiveness of the Score Filter. On the NuScenes Val dataset, IMM-MOT outperforms most other single-modal models using 3D point clouds, achieving an AMOTA of 73.8%. Our project is available at https://github.com/Ap01lo/IMM-MOT.

Related papers

• OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering [16.047505930360202]
  We present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli filter. We show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-17T09:24:26Z)
• Delving into Motion-Aware Matching for Monocular 3D Object Tracking [81.68608983602581]
  We find that the motion cue of objects along different time frames is critical in 3D multi-object tracking. We propose MoMA-M3T, a framework that mainly consists of three motion-aware components. We conduct extensive experiments on the nuScenes and KITTI datasets to demonstrate our MoMA-M3T achieves competitive performance against state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-22T17:53:58Z)
• MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion [13.125168307241765]
  We propose an end-to-end transformer-based MOT algorithm (MotionTrack) with multi-modality sensor inputs to track objects with multiple classes. The MotionTrack and its variations achieve better results (AMOTA score at 0.55) on the nuScenes dataset compared with other classical baseline models.
  arXiv  Detail & Related papers  (2023-06-29T15:00:12Z)
• TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses [51.60422927416087]
  3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. We present TrajectoryFormer, a novel point-cloud-based 3D MOT framework.
  arXiv  Detail & Related papers  (2023-06-09T13:31:50Z)
• You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking [9.20064374262956]
  The proposed framework can achieve robust tracking by using only a 2D detector and a 3D detector. It is proven more accurate than many of the state-of-the-art TBD-based multi-modal tracking methods.
  arXiv  Detail & Related papers  (2023-04-18T02:45:18Z)
• An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds [50.19288542498838]
  3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. We introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective.
  arXiv  Detail & Related papers  (2023-03-21T17:28:44Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion [34.42289908350286]
  3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection. It can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. We propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT)
  arXiv  Detail & Related papers  (2022-09-06T14:41:38Z)
• InterTrack: Interaction Transformer for 3D Multi-Object Tracking [9.283656931246645]
  3D multi-object tracking (MOT) is a key problem for autonomous vehicles. Our proposed solution, InterTrack, generates discriminative object representations for data association. We validate our approach on the nuScenes 3D MOT benchmark, where we observe significant improvements.
  arXiv  Detail & Related papers  (2022-08-17T03:24:36Z)
• Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration [56.01625477187448]
  We propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT) It takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks.
  arXiv  Detail & Related papers  (2021-05-31T03:16:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.