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In pedestrian detection intricate feature descriptors are used to improve the detection rate at the cost of computational complexity. In this paper, we propose a detector based on simple, robust edgelet features to enhance the detection rate and classifier based on k-means clustering approach to reduce computational complexity. The proposed framework consists of extraction of candidate features of pedestrian detection using edgelet features and use of the cascade structure of k-means clustering for classification enabling high detection accuracy at low false positives. Experimental results show that the proposed method requires less processing time per frame, making it suitable for real-time systems.

Introduction:The aim of the study was to evaluate the efficacy and safety of fixed-dose combination (FDC) of metoprolol, telmisartan, and chlorthalidone in patients with essential hypertension and stable coronary artery disease (CAD) who showed inadequate response to dual therapy.Methods: In this phase III, open-label, multicenter study, 254 adults with stable CAD having uncontrolled hypertension despite being treated with FDC of metoprolol (25/50 mg) and telmisartan (40 mg) were included. Patients received either of the following FDC for 24 weeks: metoprolol (25 mg), telmisartan (40 mg), and chlorthalidone (12.5 mg) (FDC1; n = 139) or metoprolol (50 mg), telmisartan (40 mg), and chlorthalidone (12.5 mg) (FDC2; n = 115) tablets once daily. The FDCs were developed using the novel Wrap Matrix TM