TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM

• URL: http://arxiv.org/abs/2210.16204v1
• Date: Fri, 28 Oct 2022 15:23:50 GMT
• Title: TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
• Authors: Nicola Marinello (1), Marc Proesmans (1 and 3), Luc Van Gool (1 and 2 and 3) ((1) KU Leuven/ESAT-PSI, (2) ETH Zurich/CVL, (3) TRACE vzw)
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system's awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking. We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors. The feature embeddings are trained to include information about the visual appearance and monocular 3D object characteristics, while motion descriptors provide a strong representation of object trajectories. We will show that our approach effectively re-identifies objects, and also behaves reliably and accurately in case of occlusions, missed detections and can detect re-appearance across different field of views. Experimental evaluation shows that our approach outperforms state-of-the-art on nuScenes by a large margin. We also obtain competitive results on KITTI.

Related papers

• SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics [0.7373617024876725]
  In autonomous driving, 3D object detection provides more precise information for downstream tasks, including path planning and motion estimation. We propose SeSame: a method aimed at enhancing semantic information in existing LiDAR-only based 3D object detection. Experiments demonstrate the effectiveness of our method with performance improvements on the KITTI object detection benchmark.
  arXiv  Detail & Related papers  (2024-03-11T08:17:56Z)
• 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)
• 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)
• High-level camera-LiDAR fusion for 3D object detection with machine learning [0.0]
  This paper tackles the 3D object detection problem, which is of vital importance for applications such as autonomous driving. It uses a Machine Learning pipeline on a combination of monocular camera and LiDAR data to detect vehicles in the surrounding 3D space of a moving platform. Our results demonstrate an efficient and accurate inference on a validation set, achieving an overall accuracy of 87.1%.
  arXiv  Detail & Related papers  (2021-05-24T01:57:34Z)
• EagerMOT: 3D Multi-Object Tracking via Sensor Fusion [68.8204255655161]
  Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods rely on depth sensors (e.g., LiDAR) to detect and track targets in 3D space, but only up to a limited sensing range due to the sparsity of the signal. We propose EagerMOT, a simple tracking formulation that integrates all available object observations from both sensor modalities to obtain a well-informed interpretation of the scene dynamics.
  arXiv  Detail & Related papers  (2021-04-29T22:30:29Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• 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)
• Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation [8.854112907350624]
  3D multi-object tracking plays a vital role in autonomous navigation. Many approaches detect objects in 2D RGB sequences for tracking, which is lack of reliability when localizing objects in 3D space. We propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames.
  arXiv  Detail & Related papers  (2020-11-25T16:14:40Z)
• Kinematic 3D Object Detection in Monocular Video [123.7119180923524]
  We propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization. We achieve state-of-the-art performance on monocular 3D object detection and the Bird's Eye View tasks within the KITTI self-driving dataset.
  arXiv  Detail & Related papers  (2020-07-19T01:15:12Z)

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.