کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
525753 869021 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A multimodal temporal panorama approach for moving vehicle detection, reconstruction and classification
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A multimodal temporal panorama approach for moving vehicle detection, reconstruction and classification
چکیده انگلیسی


• An effective multimodal temporal panorama approach for moving vehicle detection and classification using a novel sensing system.
• A new audio-visual vehicle (AVV) dataset, which features audio-visual alignment, vehicle detection and reconstruction.
• Multimodal audio-visual feature extraction and selection with systematically study for multimodal feature integration.

Moving vehicle detection and classification using multimodal data is a challenging task in data collection, audio-visual alignment, data labeling and feature selection under uncontrolled environments with occlusions, motion blurs, varying image resolutions and perspective distortions. In this work, we propose an effective multimodal temporal panorama approach for moving vehicle detection and classification using a novel long-range audio-visual sensing system. A new audio-visual vehicle (AVV) dataset is created, which features automatic vehicle detection and audio-visual alignment, accurate vehicle extraction and reconstruction, and efficient data labeling. In particular, vehicles’ visual images are reconstructed once detected in order to remove most of the occlusions, motion blurs, and variations of perspective views. Multimodal audio-visual features are extracted, including global geometric features (aspect ratios, profiles), local structure features (HOGs), as well various audio features (MFCCs, etc.). Using radial-based SVMs, the effectiveness of the integration of these multimodal features is thoroughly and systematically studied. The concept of MTP may not be only limited to visual, motion and audio modalities; it could also be applicable to other sensing modalities that can obtain data in the temporal domain.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 117, Issue 12, December 2013, Pages 1724–1735
نویسندگان
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