Deep Spatio-Temporal Representation for Detection of Road Accidents Using Stacked Autoencoder

Singh, Dinesh and C, Krishna Mohan (2018) Deep Spatio-Temporal Representation for Detection of Road Accidents Using Stacked Autoencoder. IEEE Transactions on Intelligent Transportation Systems. pp. 1-9. ISSN 1524-9050

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Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle's trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed

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Item Type: Article
Uncontrolled Keywords: Accident detection, anomaly detection, deep learning, stacked autoencoder.
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 04 Jun 2018 09:31
Last Modified: 04 Jun 2018 09:31
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