Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model

• URL: http://arxiv.org/abs/2309.14820v1
• Date: Tue, 26 Sep 2023 10:36:59 GMT
• Title: Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model
• Authors: Nianhao Xie
• Abstract summary: Three-dimensional tracking of multiple objects from multiple views has a wide range of applications. Current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Three-dimensional tracking of multiple objects from multiple views has a wide range of applications, especially in the study of bio-cluster behavior which requires precise trajectories of research objects. However, there are significant temporal-spatial association uncertainties when the objects are similar to each other, frequently maneuver, and cluster in large numbers. Aiming at such a multi-view multi-object 3D tracking scenario, a current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. The CSKPF algorithm predicts the objects' states and estimates the objects' state covariance by the current statistical model to importance particle sampling efficiency, and suppresses the measurement noise by the Kalman filter. The simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method. The real experiment on fruitfly clusters also confirms the effectiveness of the CSKPF method.

Related papers

• Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework [44.44329455757931]
  In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. To address this, we propose a multi-branch two-stage 3D object detection framework using a Semantic-aware Multi-branch Sampling (SMS) module. The experimental results on KITTI 3D object detection benchmark dataset show that our method achieves excellent detection performance improvement for a variety of backbones.
  arXiv  Detail & Related papers  (2024-07-08T09:25:45Z)
• Engineering an Efficient Object Tracker for Non-Linear Motion [0.0]
  The goal of multi-object tracking is to detect and track all objects in a scene while maintaining unique identifiers for each. This task is especially hard in case of scenarios involving dynamic and non-linear motion patterns. In this paper, we introduce DeepMoveSORT, a novel, carefully engineered multi-object tracker designed specifically for such scenarios.
  arXiv  Detail & Related papers  (2024-06-30T15:50:54Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking [3.5693768338940304]
  We propose two innovative data-driven filtering methods for tracking-by-detection systems. The first method employs a Bayesian filter with a trainable motion model to predict an object's future location. The second method, an end-to-end trainable filter, goes a step further by learning to correct detector errors.
  arXiv  Detail & Related papers  (2024-02-15T10:47:44Z)
• PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection [66.94819989912823]
  We propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection. We use point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement. We conduct extensive experiments on the large-scale dataset to demonstrate that our approach performs well against state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-13T18:59:13Z)
• DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking [67.34803048690428]
  We propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. It is flexible and practical that can be plugged into most camera-based 3D object detectors.
  arXiv  Detail & Related papers  (2023-03-29T12:33:55Z)
• 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)
• Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving [62.053071723903834]
  Multi-object state estimation is a fundamental problem for robotic applications. We consider learning maximum-likelihood parameters using particle methods. We apply our method to real data collected from autonomous vehicles.
  arXiv  Detail & Related papers  (2022-12-14T01:21:05Z)
• Segment as Points for Efficient Online Multi-Object Tracking and Segmentation [66.03023110058464]
  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. Our method generates a new tracking-by-points paradigm where discriminative instance embeddings are learned from randomly selected points rather than images. The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2020-07-03T08:29:35Z)
• Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking [1.8275108630751837]
  In this paper, we employ Bayesian nonparametric methods to address challenges in tracking multiple objects. We propose modeling the multiple object parameter state prior using the dependent Dirichlet and Pitman-Yor processes. These nonparametric models have been shown to be more flexible and robust, when compared to existing methods.
  arXiv  Detail & Related papers  (2020-04-22T19:07:35Z)
• Probabilistic 3D Multi-Object Tracking for Autonomous Driving [23.036619327925088]
  We present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge. Our method estimates the object states by adopting a Kalman Filter. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method.
  arXiv  Detail & Related papers  (2020-01-16T06:38:02Z)

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.