Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy

• URL: http://arxiv.org/abs/2002.09554v1
• Date: Fri, 21 Feb 2020 21:21:58 GMT
• Title: Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy
• Authors: Ziyuan Liu, Dongheui Lee, Wolfgang Sepp
• Abstract summary: The proposed system tracks human motion based on monocular silhouette-matching. A new 3D articulated human upper body model with the name 3D cardbox model is created and is proven to work successfully for motion tracking.
• Score: 8.894218894797977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The challenge of markerless human motion tracking is the high dimensionality of the search space. Thus, efficient exploration in the search space is of great significance. In this paper, a motion capturing algorithm is proposed for upper body motion tracking. The proposed system tracks human motion based on monocular silhouette-matching, and it is built on the top of a hierarchical particle filter, within which a novel deterministic resampling strategy (DRS) is applied. The proposed system is evaluated quantitatively with the ground truth data measured by an inertial sensor system. In addition, we compare the DRS with the stratified resampling strategy (SRS). It is shown in experiments that DRS outperforms SRS with the same amount of particles. Moreover, a new 3D articulated human upper body model with the name 3D cardbox model is created and is proven to work successfully for motion tracking. Experiments show that the proposed system can robustly track upper body motion without self-occlusion. Motions towards the camera can also be well tracked.

Related papers

• DELTA: Dense Efficient Long-range 3D Tracking for any video [82.26753323263009]
  We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space.
  arXiv  Detail & Related papers  (2024-10-31T17:59:01Z)
• SLAM assisted 3D tracking system for laparoscopic surgery [22.36252790404779]
  This work proposes a real-time monocular 3D tracking algorithm for post-registration tasks. Experiments from in-vivo and ex-vivo tests demonstrate that the proposed 3D tracking system provides robust 3D tracking.
  arXiv  Detail & Related papers  (2024-09-18T04:00:54Z)
• 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)
• Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model [18.139630622759636]
  We present AGRoL, a novel conditional diffusion model specifically designed to track full bodies given sparse upper-body tracking signals. Our model is based on a simple multi-layer perceptron (MLP) architecture and a novel conditioning scheme for motion data. Unlike common diffusion architectures, our compact architecture can run in real-time, making it suitable for online body-tracking applications.
  arXiv  Detail & Related papers  (2023-04-17T19:35:13Z)
• TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM [0.0]
  3D object tracking is a critical task in autonomous driving systems. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking.
  arXiv  Detail & Related papers  (2022-10-28T15:23:50Z)
• A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds [50.54083964183614]
  It is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. We propose DMT, a Detector-free Motion prediction based 3D Tracking network that totally removes the usage of complicated 3D detectors.
  arXiv  Detail & Related papers  (2022-03-08T17:49:07Z)
• MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks [77.56526918859345]
  We present a novel framework that brings the 3D motion task from controlled environments to in-the-wild scenarios. It is capable of body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure.
  arXiv  Detail & Related papers  (2021-12-19T07:52:05Z)
• Recovering and Simulating Pedestrians in the Wild [81.38135735146015]
  We propose to recover the shape and motion of pedestrians from sensor readings captured in the wild by a self-driving car driving around. We incorporate the reconstructed pedestrian assets bank in a realistic 3D simulation system. We show that the simulated LiDAR data can be used to significantly reduce the amount of real-world data required for visual perception tasks.
  arXiv  Detail & Related papers  (2020-11-16T17:16:32Z)
• Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter [4.189643331553922]
  IMU-based systems, as well as Marker-based motion tracking systems, are the most popular methods to track movement due to their low cost of implementation and lightweight. This paper proposes a quaternion-based Extended Kalman filter approach to recover the human leg segments motions with a set of IMU sensors data fused with camera-marker system data.
  arXiv  Detail & Related papers  (2020-11-01T17:54:53Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds [22.850519892606716]
  We have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories.
  arXiv  Detail & Related papers  (2020-05-06T10:04:23Z)

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.