Labits: Layered Bidirectional Time Surfaces Representation for Event Camera-based Continuous Dense Trajectory Estimation

• URL: http://arxiv.org/abs/2412.08849v1
• Date: Thu, 12 Dec 2024 01:11:50 GMT
• Title: Labits: Layered Bidirectional Time Surfaces Representation for Event Camera-based Continuous Dense Trajectory Estimation
• Authors: Zhongyang Zhang, Jiacheng Qiu, Shuyang Cui, Yijun Luo, Tauhidur Rahman,
• Abstract summary: Event cameras capture dynamic scenes with high temporal resolution and low latency. We introduce Labits: Layered Bidirectional Time Surfaces, a simple yet elegant representation designed to retain all these features. Our approach achieves an impressive 49% reduction in trajectory end-point error (TEPE) compared to the previous state-of-the-art on the MultiFlow dataset.
• Score: 1.3416369506987165
• License:
• Abstract: Event cameras provide a compelling alternative to traditional frame-based sensors, capturing dynamic scenes with high temporal resolution and low latency. Moving objects trigger events with precise timestamps along their trajectory, enabling smooth continuous-time estimation. However, few works have attempted to optimize the information loss during event representation construction, imposing a ceiling on this task. Fully exploiting event cameras requires representations that simultaneously preserve fine-grained temporal information, stable and characteristic 2D visual features, and temporally consistent information density, an unmet challenge in existing representations. We introduce Labits: Layered Bidirectional Time Surfaces, a simple yet elegant representation designed to retain all these features. Additionally, we propose a dedicated module for extracting active pixel local optical flow (APLOF), significantly boosting the performance. Our approach achieves an impressive 49% reduction in trajectory end-point error (TEPE) compared to the previous state-of-the-art on the MultiFlow dataset. The code will be released upon acceptance.

Related papers

• Event-based Motion Deblurring via Multi-Temporal Granularity Fusion [5.58706910566768]
  Event camera, a bio-inspired sensor offering continuous visual information could enhance the deblurring performance. Existing event-based image deblurring methods usually utilize voxel-based event representations. We introduce point cloud-based event representation into the image deblurring task and propose a Multi-Temporal Granularity Network (MTGNet) It combines the spatially dense but temporally coarse-grained voxel-based event representation and the temporally fine-grained but spatially sparse point cloud-based event.
  arXiv  Detail & Related papers  (2024-12-16T15:20:54Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point. It tackles the challenges posed by spatial sparsity and motion sensitivity in events. It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• Motion Segmentation for Neuromorphic Aerial Surveillance [42.04157319642197]
  Event cameras offer superior temporal resolution, superior dynamic range, and minimal power requirements. Unlike traditional frame-based sensors that capture redundant information at fixed intervals, event cameras asynchronously record pixel-level brightness changes. We introduce a novel motion segmentation method that leverages self-supervised vision transformers on both event data and optical flow information.
  arXiv  Detail & Related papers  (2024-05-24T04:36:13Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• Recovering Continuous Scene Dynamics from A Single Blurry Image with Events [58.7185835546638]
  An Implicit Video Function (IVF) is learned to represent a single motion blurred image with concurrent events. A dual attention transformer is proposed to efficiently leverage merits from both modalities. The proposed network is trained only with the supervision of ground-truth images of limited referenced timestamps.
  arXiv  Detail & Related papers  (2023-04-05T18:44:17Z)
• Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation [79.02808071245634]
  Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars.
  arXiv  Detail & Related papers  (2023-03-17T12:12:41Z)
• Motion-inductive Self-supervised Object Discovery in Videos [99.35664705038728]
  We propose a model for processing consecutive RGB frames, and infer the optical flow between any pair of frames using a layered representation. We demonstrate superior performance over previous state-of-the-art methods on three public video segmentation datasets.
  arXiv  Detail & Related papers  (2022-10-01T08:38:28Z)
• Motion-aware Memory Network for Fast Video Salient Object Detection [15.967509480432266]
  We design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.
  arXiv  Detail & Related papers  (2022-08-01T15:56:19Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)

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.