Online Multi-View Naturalistic Driving Temporal Action LocalizationPublic Deposited
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Distracted driving behavior is a major concern and claims the lives of many every year. To address the issue, AI City has presented the Track 3 Challenge, which aims to encourage research and development towards solutions that recognize and localize when distracted driving behavior occurs in time. We present a novel online, multi-view architecture that aggregates temporal context and uniquely samples past and present events to label and pinpoint the start and end temporal boundaries of distracted driving actions. Our algorithm is a two-stage Temporal Action Localization (TAL) method, which does not require a boundary detection network or any localization training. It consists of several stages to predict and refine temporal boundaries: aggregation, prediction, consolidation, post-localization processing, and assessment. Furthermore, our method achieves top results in the AI City 2023 Track 3 Challenge and performs highly in run-time efficiency. Our code is available at https://github.com/CarrotPeeler/WPI-Naturalistic-Driving-Action-Recognition-MQP.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
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