Nighttime Motion Vehicle Detection Based on Multiple Instance Learning

Abstract:

In this paper, we propose an effective approach for detecting moving vehicles in nighttime traffic scenes. We use Multiple Instance Learning method to automatically detect vehicle from video sequences by constructing the Multiple Instance Learning model at nighttime. At first, we extract SIFT feature using SIFT feature extraction algorithm, which is used to characterize moving vehicles at nighttime. Then Multiple Instance Learning model is used for the on-road detection of vehicles at nighttime, in order to improve the detection accuracy, the class label information was used for the learning of the Multiple Instance Learning model. Final experiments were performed and evaluate the proposed method at nighttime under urban traffic condition, the experiment results show that the average detection accuracy is over 96.2%, which validates that the proposed vehicle detection approach is feasible and effective for the on-road detection of vehicles at nighttime and identification in various nighttime environments.

• Periodical: Applied Mechanics and Materials (Volume 529)
• Pages: 370-374
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.529.370
• Online since: June 2014
• Authors: Shao Ping Zhu*
• Keywords: Multiple Instance Learning, SIFT Feature Extraction, Vehicle Detection

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