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This paper proposes a method for detecting vehicles in urban traffic. The proposed method extracts vehicle candidates using AdaBoost. The candidate extraction process was speeded up further, exploiting inverse perspective transform matrix. Then the vehicle candidates were verified by the existence of vertical and horizontal edges. The detected vehicle regions were corrected by the vertical edges and...
This paper targets at detecting preceding vehicles in a wide range of distance. We propose an Adaboost-based approach combined with hierarchical image and sub-window scaling schemes. The relationship is investigated among object characteristics, image structures and image scales. A parameter set is developed to easily adjust overall performance, which benefits researchers to establish a vehicle detection...
We present a two-layer night time vehicle detector in this work. At the first layer, vehicle headlight detection is applied to find areas (bounding boxes) where the possible pairs of headlights locate in the image, the Haar feature based AdaBoost framework is then applied to detect the vehicle front. This approach has achieved a very promising performance for vehicle detection at night time. Our results...
In this paper, we firstly describe improved Adaboost algorithm, and then introduce wavelet moment, which has rotation, shift, scale moment invariants and multi-resolution characteristic, and which can not only extract image local feature, but also can extract global feature. so it is more stronger to oppose noise. This paper proposes dynamic pyramid changing idea of detector scale. A cascade classifier...
This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9)...
We present a real-time solution for pedestrian detection in images. The key point of such method is the definition of a generic model able to describe the huge variability of pedestrians. We propose a learning based approach using a training set composed by positive and negative samples. A simple description of each candidate image provides a huge feature vector from which can be built weak classifiers...
This paper discusses a system for classification of a vehicle passenger for air bag control, using 3D-images from a time-of-flight measuring sensor. Radial depth is determined using a photonic mixer device (PMD) with suppression of background illumination for near infrared wavelengths. The occupant region is determined by fitting a gradient based model for the seat shape and surface features are extracted...
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