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Pedestrian detection by camera sensor is an important function in intelligent vehicle. Histograms of Oriented Gradients (HOG) features is a kind of efficient pedestrian feature. We optimized the HOG features to achieve an accurate human detection system. We don't normalize the input detection windows but resize the cell and block by same ratio. In the processing of calculate the HOG features, the...
This paper presents a road signs detection, recognition and tracking system based on multi-cues hybrid. In detection stage, the color and gradient cues are used to segment the interesting regions, and the corner and geometrical cues are used to detect the signs. A pseudo RGB-HSI conversion method without the need of nonlinear transformation is presented for color extraction. In recognition stage,...
A real-time monocular vision based rear vehicle and motorcycle detection and tracking approach is presented for lane change assistant (LCA). To achieve robustness and accuracy this work detects and tracks multiple vehicles and motorcycles on road by combining multiple cues. To achieve real-time multi-resolution technology is used to reduce computing complexity, and all algorithms have been implemented...
Clustered microcalcification is an important signal for breast cancer in the early stages. In this paper, we propose a multiple kernel SVM with group features (GF-SVM) to tackle problems associated with heterogeneous features of clustered microcalcification and normal breast tissues in suspicious regions. Specifically, different types of features such as being gradient, geometric and textural are...
A monocular vision based detection algorithm is presented to detect rear vehicles. Our detection algorithm consist of two main steps: knowledge based hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, a shadow extraction method is proposed based on contrast sensitivity to extract regions of interest (ROI), it can effectively solve the problems caused...
A motorcycle detection algorithm is proposed to solve imbalanced datasets in motorcycle recognition based on SVM ensembles. Moreover, an improved Wavelet feature algorithm is also presented. Experimental results show that the presented method has high precision and recall. Furthermore, the system performance can also be improved by increasing learning.
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