Traffic Accident Benchmark for Causality Recognition
| Autor: | Bohyung Han, Tackgeun You |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject ComputerApplications_COMPUTERSINOTHERSYSTEMS Ambiguity Machine learning computer.software_genre Causality Task (project management) Accident (fallacy) Anticipation (artificial intelligence) Benchmark (computing) Artificial intelligence business Baseline (configuration management) computer media_common Codebase |
| Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585709 ECCV (7) |
| Popis: | We propose a brand new benchmark for analyzing causality in traffic accident videos by decomposing an accident into a pair of events, cause and effect. We collect videos containing traffic accident scenes and annotate cause and effect events for each accident with their temporal intervals and semantic labels; such annotations are not available in existing datasets for accident anticipation task. Our dataset has the following two advantages over the existing ones, which would facilitate practical research for causality analysis. First, the decomposition of an accident into cause and effect events provides atomic cues for reasoning on a complex environment and planning future actions. Second, the prediction of cause and effect in an accident makes a system more interpretable to humans, which mitigates the ambiguity of legal liabilities among agents engaged in the accident. Using the proposed dataset, we analyze accidents by localizing the temporal intervals of their causes and effects and classifying the semantic labels of the accidents. The dataset as well as the implementations of baseline models are available in the code repository (https://github.com/tackgeun/CausalityInTrafficAccident). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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