Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets
- URL: http://arxiv.org/abs/2407.08872v1
- Date: Thu, 11 Jul 2024 21:15:21 GMT
- Title: Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets
- Authors: Linh Van Ma, Tran Thien Dat Nguyen, Changbeom Shim, Du Yong Kim, Namkoo Ha, Moongu Jeon,
- Abstract summary: This paper proposes an online visual multi-object tracking (MOT) algorithm that resolves object appearance-reappearance and occlusion.
Our solution is based on the labeled random finite set (LRFS) filtering approach.
We propose a fuzzy detection model that takes into consideration the overlapping areas between tracks and their sizes.
- Score: 10.618186767487993
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes an online visual multi-object tracking (MOT) algorithm that resolves object appearance-reappearance and occlusion. Our solution is based on the labeled random finite set (LRFS) filtering approach, which in principle, addresses disappearance, appearance, reappearance, and occlusion via a single Bayesian recursion. However, in practice, existing numerical approximations cause reappearing objects to be initialized as new tracks, especially after long periods of being undetected. In occlusion handling, the filter's efficacy is dictated by trade-offs between the sophistication of the occlusion model and computational demand. Our contribution is a novel modeling method that exploits object features to address reappearing objects whilst maintaining a linear complexity in the number of detections. Moreover, to improve the filter's occlusion handling, we propose a fuzzy detection model that takes into consideration the overlapping areas between tracks and their sizes. We also develop a fast version of the filter to further reduce the computational time.
Related papers
- Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking [12.389483990547223]
We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras.
We exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density.
arXiv Detail & Related papers (2024-05-28T21:36:16Z) - Object-Centric Multiple Object Tracking [124.30650395969126]
This paper proposes a video object-centric model for multiple-object tracking pipelines.
It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module.
Benefited from object-centric learning, we only require sparse detection labels for object localization and feature binding.
arXiv Detail & Related papers (2023-09-01T03:34:12Z) - Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object
Tracking [38.36872739816151]
Occlusion-Aware Attention (OAA) module in the detector highlights the object features while suppressing the occluded background regions.
OAA can serve as a modulator that enhances the detector for some potentially occluded objects.
We design a Re-ID embedding matching block based on the optimal transport problem.
arXiv Detail & Related papers (2023-08-30T06:56:53Z) - Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection.
First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network.
Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
arXiv Detail & Related papers (2023-07-01T13:53:14Z) - Real-time Multi-Object Tracking Based on Bi-directional Matching [0.0]
This study offers a bi-directional matching algorithm for multi-object tracking.
A stranded area is used in the matching algorithm to temporarily store the objects that fail to be tracked.
In the MOT17 challenge, the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking speed.
arXiv Detail & Related papers (2023-03-15T08:38:08Z) - DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection [0.0]
RCNN-based pedestrian detectors use rectangle regions to extract instance features.
The number of severely overlapping objects and the number of slightly overlapping objects are unbalanced.
An iterable dynamic instance noise filter (DINF) is proposed for the RCNN-based pedestrian detectors to improve the signal-noise ratio of the instance feature.
arXiv Detail & Related papers (2023-01-13T14:12:36Z) - OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object
Detection [51.153003057515754]
OPA-3D is a single-stage, end-to-end, Occlusion-Aware Pixel-Wise Aggregation network.
It jointly estimates dense scene depth with depth-bounding box residuals and object bounding boxes.
It outperforms state-of-the-art methods on the main Car category.
arXiv Detail & Related papers (2022-11-02T14:19:13Z) - High-resolution Iterative Feedback Network for Camouflaged Object
Detection [128.893782016078]
Spotting camouflaged objects that are visually assimilated into the background is tricky for object detection algorithms.
We aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries.
We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner.
arXiv Detail & Related papers (2022-03-22T11:20:21Z) - Online Multi-Object Tracking with delta-GLMB Filter based on Occlusion
and Identity Switch Handling [1.713291434132985]
We propose an online multi-object tracking (MOT) method in a delta Generalized Labeled Multi-Bernoulli (delta-GLMB) filter framework.
To handle occlusion and miss-detection issues, we propose a measurement-to-disappeared track association method.
We evaluate the proposed method on well-known and publicly available MOT15 and MOT17 test datasets.
arXiv Detail & Related papers (2020-11-19T21:38:40Z) - Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
We propose a novel unsupervised learning approach to 3D shape correspondence.
We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
arXiv Detail & Related papers (2020-10-28T22:24:07Z) - Towards Accurate Pixel-wise Object Tracking by Attention Retrieval [50.06436600343181]
We propose an attention retrieval network (ARN) to perform soft spatial constraints on backbone features.
We set a new state-of-the-art on recent pixel-wise object tracking benchmark VOT 2020 while running at 40 fps.
arXiv Detail & Related papers (2020-08-06T16:25:23Z)
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.