Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model

• URL: http://arxiv.org/abs/2403.11978v1
• Date: Mon, 18 Mar 2024 17:13:18 GMT
• Title: Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model
• Authors: Jan Krejčí, Oliver Kost, Ondřej Straka, Jindřich Duník,
• Abstract summary: It is assumed that the extent of the moving object can be described via known statistics in 3D, such as pedestrian height. A nonlinear filter for this model is implemented using the unscented Kalman filter (UKF) and tested using the publicly available MOT-17 dataset. The proposed solution yields promising results in 3D while maintaining perfect results when projected into the 2D image.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A first-principle single-object model is proposed for pedestrian tracking. It is assumed that the extent of the moving object can be described via known statistics in 3D, such as pedestrian height. The proposed model thus need not constrain the object motion in 3D to a common ground plane, which is usual in 3D visual tracking applications. A nonlinear filter for this model is implemented using the unscented Kalman filter (UKF) and tested using the publicly available MOT-17 dataset. The proposed solution yields promising results in 3D while maintaining perfect results when projected into the 2D image. Moreover, the estimation error covariance matches the true one. Unlike conventional methods, the introduced model parameters have convenient meaning and can readily be adjusted for a problem.

Related papers

• Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields [98.15456815880911]
  We propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry data. As a generic distance metric, DirDist has the potential to advance the field of 3D geometric modeling.
  arXiv  Detail & Related papers  (2024-01-18T05:31:53Z)
• 6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics [19.64111218032901]
  We present a novel technique to estimate the 6D pose of objects from single images. We employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. Our method achieves state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021 post-mortem competition.
  arXiv  Detail & Related papers  (2023-03-23T13:18:05Z)
• Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [78.76508318592552]
  Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. Most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. We propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images.
  arXiv  Detail & Related papers  (2023-03-15T15:14:00Z)
• Homography Loss for Monocular 3D Object Detection [54.04870007473932]
  A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information. Our method yields the best performance compared with the other state-of-the-arts by a large margin on KITTI 3D datasets.
  arXiv  Detail & Related papers  (2022-04-02T03:48:03Z)
• DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [43.02373021724797]
  We introduce a framework for multi-camera 3D object detection. Our method manipulates predictions directly in 3D space. We achieve state-of-the-art performance on the nuScenes autonomous driving benchmark.
  arXiv  Detail & Related papers  (2021-10-13T17:59:35Z)
• AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [15.244852122106634]
  We propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed.
  arXiv  Detail & Related papers  (2021-08-25T08:50:06Z)
• Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows [24.0966076588569]
  We propose a normalizing flow based method that exploits the deterministic 3D-to-2D mapping to solve the ambiguous inverse 2D-to-3D problem. We evaluate our approach on the two benchmark datasets Human3.6M and MPI-INF-3DHP, outperforming all comparable methods in most metrics.
  arXiv  Detail & Related papers  (2021-07-29T07:33:14Z)
• A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars [0.0]
  In this paper, the YOLOv3 approach is utilized beside an OpenCV tracker to elicit features from an image. To obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation filter. A switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter.
  arXiv  Detail & Related papers  (2021-03-24T18:21:29Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)
• PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
  Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space. We propose a model that unifies these two tasks in the same metric space. Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
  arXiv  Detail & Related papers  (2021-01-17T05:11:38Z)
• 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)

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.