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In this paper, the framework is presented for using active learning to train a robust monocular on-road vehicle detector for active safety, based on Adaboost classification and Haar-like rectangular image features. An initial vehicle detector was trained using Adaboost and Haar-like rectangular image features and was very susceptible to false positives. This detector was run on an independent highway...
This paper discusses the issues and approaches involved in developing a mobile vehicle-mounted system to detect, classify, and log the surrounding vehicles in a database for efficient query-based retrieval. This system consists of three components: (1) vehicle sensing, detection, and tracking (2) feature extraction and classification (3) database storage and retrieval. Relevant research in each of...
This paper presents an overview of a novel multimodal system being developed at UC San Diego for vehicle detection and traffic flow analysis. A distributed multimodal array (DiMMA) framework is presented for sensory data acquisition, processing, analysis, fusion, and "active" control mechanisms needed to recognize objects, events, and activities which have multi-modal signatures. Current...
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