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In response to the surge in motor vehicle accidents, this project introduces an advanced car crash detection system leveraging both video and audio data from dashboard cameras. Unlike existing systems relying on a single data type, this system combines diverse data to enhance detection accuracy by capturing unique perspectives.
The system applies deep learning techniques, integrating gated recurrent units (GRU) and convolutional neural networks (CNN). Experimental validation using YouTube clips featuring car accidents demonstrated significant performance improvements over traditional single-modal classifiers.
Envisioned integration into an emergency road call service allows automatic accident recognition and prompt transmission of relevant information to aid emergency recovery agencies, facilitating immediate rescue operations.
Several studies historically focused on vision-based accident detection using CCD cameras, linear discriminant analysis (LDA), SVMs, and deep learning models like RNNs and CNNs.
Audio-based detection systems have emerged, utilizing CNN architectures to analyze spectrogram images of environmental sounds, demonstrating promising results.
Recent research highlights multimodal ensemble deep learning models combining video and audio data, outperforming single classifiers, and showcasing real-time capabilities.
While previous studies focused on single-modal approaches, the integration of both video and audio data remains largely unexplored. This combined multimodal approach presents a promising avenue for enhancing detection performance.
This README provides an overview of an innovative car crash detection system, integrating video and audio data to improve accuracy and aid in emergency response.
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