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Traditional vehicle detectors always utilize singletemplate model to represent the vehicle which can not encircle vehicles with different aspect ratios. In this paper, we propose a fast and accurate approach for detecting vehicles which joints classification and aspect ratio regression. The key idea is extending the boosting decision trees method to estimate vehicle's aspect ratio during vehicle detection,...
Accurate pedestrian detection with high speed is always of great interests especially for practical application. Detectors usually follow the feature selection paradigm, and need to first construct rich and diverse features. In particular, current state-of-the-arts generate more channels of feature by convolving the basic feature channels with filter banks, which significantly improves accuracy. In...
Photogrammetric context captures the relationship between object heights and camera viewpoint, and can be used to reject false detections that appear in wrong locations or scales. In this work, we address the problem of using photogram-metric constraints in object detection when camera poses are unknown. We propose a model to capture both local appearance features and global photogrammetric context,...
In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field...
Localization is of vital importance to a mobile vehicle system. Most of the existing algorithms are based on laser range finders, sonar sensors, artificial landmarks or GPS information. In this paper, we present a sequential probability location method for mobile vehicle, which uses scale-invariant image features as natural landmarks in unmodified environments. First, we construct a ground truth map...
This paper presents an central difference Kalman filter (CDKF) based simultaneous localization and mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent...
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