Event Data Association via Robust Model Fitting for Event-based Object Tracking

• URL: http://arxiv.org/abs/2110.12962v2
• Date: Tue, 9 Apr 2024 16:39:00 GMT
• Title: Event Data Association via Robust Model Fitting for Event-based Object Tracking
• Authors: Haosheng Chen, Shuyuan Lin, Yan Yan, Hanzi Wang, Xinbo Gao,
• Abstract summary: We propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem. The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion. The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.
• Score: 66.05728523166755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event-based approaches, which are based on bio-inspired asynchronous event cameras, have achieved promising performance on various computer vision tasks. However, the study of the fundamental event data association problem is still in its infancy. In this paper, we propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem. The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion. In EDA, we first asynchronously fuse the event data based on its information entropy. Then, we introduce a deterministic model hypothesis generation strategy, which effectively generates model hypotheses from the fused events, to represent the corresponding event trajectories. After that, we present a two-stage weighting algorithm, which robustly weighs and selects true models from the generated model hypotheses, through multi-structural geometric model fitting. Meanwhile, we also propose an adaptive model selection strategy to automatically determine the number of the true models. Finally, we use the selected true models to associate and fuse the event data, without being affected by sensor noise and irrelevant structures. We evaluate the performance of the proposed EDA on the object tracking task. The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Probabilistic Modeling for Sequences of Sets in Continuous-Time [14.423456635520084]
  We develop a general framework for modeling set-valued data in continuous-time. We also develop inference methods that can use such models to answer probabilistic queries.
  arXiv  Detail & Related papers  (2023-12-22T20:16:10Z)
• Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference [21.51753838306655]
  Temporal point processes(TPPs) are the standard method for modeling such. Existing TPP models have focused on the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. We propose a novel approach that leverages a Neural Inference (NRI) to learn a graph that infers interactions while simultaneously learning dynamics patterns from observational data.
  arXiv  Detail & Related papers  (2023-09-06T09:47:03Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• Conditional Generation of Temporally-ordered Event Sequences [29.44608199294757]
  We present a conditional generation model capable of capturing event cooccurrence as well as temporality of event sequences. This single model can address both temporal ordering, sorting a given sequence of events into the order they occurred, and event infilling, predicting new events which fit into a temporally-ordered sequence of existing ones.
  arXiv  Detail & Related papers  (2020-12-31T18:10:18Z)
• Few-Shot Event Detection with Prototypical Amortized Conditional Random Field [8.782210889586837]
  Event Detection tends to struggle when it needs to recognize novel event types with a few samples. We present a novel unified joint model which converts the task to a few-shot tagging problem with a double-part tagging scheme. We conduct experiments on the benchmark dataset FewEvent and the experimental results show that the tagging based methods are better than existing pipeline and joint learning methods.
  arXiv  Detail & Related papers  (2020-12-04T01:11:13Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

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.