Heteroskedastic Geospatial Tracking with Distributed Camera Networks
- URL: http://arxiv.org/abs/2306.02407v1
- Date: Sun, 4 Jun 2023 16:55:38 GMT
- Title: Heteroskedastic Geospatial Tracking with Distributed Camera Networks
- Authors: Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani
Srivastava, Benjamin M. Marlin
- Abstract summary: We focus on the geospatial object tracking problem using data from a distributed camera network.
The goal is to predict an object's track in geospatial coordinates along with uncertainty over the object's location.
We present a novel single-object geospatial tracking data set that includes high-accuracy ground truth object locations and video data from a network of four cameras.
- Score: 15.938635024739845
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual object tracking has seen significant progress in recent years.
However, the vast majority of this work focuses on tracking objects within the
image plane of a single camera and ignores the uncertainty associated with
predicted object locations. In this work, we focus on the geospatial object
tracking problem using data from a distributed camera network. The goal is to
predict an object's track in geospatial coordinates along with uncertainty over
the object's location while respecting communication constraints that prohibit
centralizing raw image data. We present a novel single-object geospatial
tracking data set that includes high-accuracy ground truth object locations and
video data from a network of four cameras. We present a modeling framework for
addressing this task including a novel backbone model and explore how
uncertainty calibration and fine-tuning through a differentiable tracker affect
performance.
Related papers
- Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera [19.204896246140155]
Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur.
We propose a line-based robust pose estimation and tracking method for planar or non-planar objects using an event camera.
arXiv Detail & Related papers (2024-08-06T14:36:43Z) - CXTrack: Improving 3D Point Cloud Tracking with Contextual Information [59.55870742072618]
3D single object tracking plays an essential role in many applications, such as autonomous driving.
We propose CXTrack, a novel transformer-based network for 3D object tracking.
We show that CXTrack achieves state-of-the-art tracking performance while running at 29 FPS.
arXiv Detail & Related papers (2022-11-12T11:29:01Z) - Object Detection in Aerial Images with Uncertainty-Aware Graph Network [61.02591506040606]
We propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects.
We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet)
arXiv Detail & Related papers (2022-08-23T07:29:03Z) - Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network.
We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information.
To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
arXiv Detail & Related papers (2022-04-27T13:10:48Z) - Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous
Vehicles [17.12321292167318]
It is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras.
This work presents a new Global Association Graph Model with Link Prediction approach to predict existing tracklets location and link detections with tracklets.
Our model exploits to improve the detection accuracy of a standard 3D object detector in the nuScenes detection challenge.
arXiv Detail & Related papers (2022-04-19T22:50:36Z) - Detecting and Tracking Small and Dense Moving Objects in Satellite
Videos: A Benchmark [30.078513715446196]
We build a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking.
This dataset is collected by the Jilin-1 satellite constellation.
We establish the first public benchmark for moving object detection and tracking in satellite videos.
arXiv Detail & Related papers (2021-11-25T08:01:41Z) - Learning to Track Object Position through Occlusion [32.458623495840904]
Occlusion is one of the most significant challenges encountered by object detectors and trackers.
We propose a tracking-by-detection approach that builds upon the success of region based video object detectors.
Our approach achieves superior results on a dataset of furniture assembly videos collected from the internet.
arXiv Detail & Related papers (2021-06-20T22:29:46Z) - Learning to Track with Object Permanence [61.36492084090744]
We introduce an end-to-end trainable approach for joint object detection and tracking.
Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
arXiv Detail & Related papers (2021-03-26T04:43:04Z) - Self-supervised Segmentation via Background Inpainting [96.10971980098196]
We introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera.
We exploit a self-supervised loss function that we exploit to train a proposal-based segmentation network.
We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.
arXiv Detail & Related papers (2020-11-11T08:34:40Z) - Applying r-spatiogram in object tracking for occlusion handling [16.36552899280708]
The aim of video tracking is to accurately locate a moving target in a video sequence and discriminate target from non-targets in the feature space of the sequence.
In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e. object modeling, object detection and localization, and model updating.
arXiv Detail & Related papers (2020-03-18T02:42:51Z) - Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for
Event-based Object Tracking [87.0297771292994]
We propose an Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking.
To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm.
We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD.
arXiv Detail & Related papers (2020-02-13T15:58:31Z)
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.