Track, Check, Repeat: An EM Approach to Unsupervised Tracking

• URL: http://arxiv.org/abs/2104.03424v1
• Date: Wed, 7 Apr 2021 22:51:39 GMT
• Title: Track, Check, Repeat: An EM Approach to Unsupervised Tracking
• Authors: Adam W. Harley, Yiming Zuo, Jing Wen, Ayush Mangal, Shubhankar Potdar, Ritwick Chaudhry, Katerina Fragkiadaki
• Abstract summary: We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. We learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI.
• Score: 20.19397660306534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yet-unlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.

Related papers

• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection [10.716585855033347]
  We develop an unsupervised component segmentation technique that generates training labels for a lightweight segmentation network without human labeling. We achieve a detection AUROC of 95.3% in the MVTec LOCO AD dataset, which surpasses previous SOTA methods.
  arXiv  Detail & Related papers  (2024-08-28T08:27:41Z)
• Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [108.672972439282]
  We introduce a novel decoupled pseudo-labeling (DPL) approach for SSM3OD. Our approach features a Decoupled Pseudo-label Generation (DPG) module, designed to efficiently generate pseudo-labels. We also present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels.
  arXiv  Detail & Related papers  (2024-03-26T05:12:18Z)
• 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)
• Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy [52.03068246508119]
  We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. IMAS achieves Improved UVOS with Motion-Appearance Synergy. We demonstrate its effectiveness in tuning critical hyperparams previously tuned with human annotation or hand-crafted hyperparam-specific metrics.
  arXiv  Detail & Related papers  (2022-12-17T06:47:30Z)
• Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning [59.64695628433855]
  We propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels. Our method even outperforms the state-of-the-art fully supervised competitors with less than 1% actively selected annotations.
  arXiv  Detail & Related papers  (2022-09-16T07:59:04Z)
• A two-stage data association approach for 3D Multi-object Tracking [0.0]
  We adapt a two-stage dataassociation method which was successful in image-based tracking to the 3D setting. Our method outperforms the baseline using one-stagebipartie matching for data association by achieving 0.587 AMOTA in NuScenes validation set.
  arXiv  Detail & Related papers  (2021-01-21T15:50:17Z)
• "What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences [27.915309216800125]
  We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. We propose a single, end-to-end trainable architecture which jointly incorporates motion cues and semantic knowledge. Our method neither depends on any visual registration of a kinematic robot or 3D object models, nor on precise hand-eye calibration or any additional sensor data.
  arXiv  Detail & Related papers  (2020-11-06T10:55:28Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

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.