Car detection by fusion of HOG and causal MRF

Madhogaria S, Baggenstoss PM, Schikora M, Koch W, Cremers D (2015)


Publication Type: Journal article

Publication year: 2015

Journal

Book Volume: 51

Pages Range: 575-590

Article Number: 7073514

Journal Issue: 1

DOI: 10.1109/TAES.2014.120141

Abstract

Detection of cars has a high variety of civil and military applications, e.g., transportation control, traffic monitoring, and surveillance. It forms an important aspect in the deployment of autonomous unmanned aerial systems in rescue or surveillance missions. In this paper, we present a two-stage algorithm for detecting automobiles in aerial digital images. In the first stage, a feature-based detection is performed, based on local histogram of oriented gradients and support vector machine classification. Next, a generative statistical model is used to generate a ranking for each patch. The ranking can be used as a measure of confidence or a threshold to eliminate those patches that are least likely to be an automobile. We analyze the results obtained from three different types of data sets. In various experiments, we present the performance improvement of this approach compared to a discriminative-only approach; the false alarm rate is reduced by a factor of 7 with only a 10% drop in the recall rate.

Involved external institutions

How to cite

APA:

Madhogaria, S., Baggenstoss, P.M., Schikora, M., Koch, W., & Cremers, D. (2015). Car detection by fusion of HOG and causal MRF. IEEE Transactions on Aerospace and Electronic Systems, 51(1), 575-590. https://dx.doi.org/10.1109/TAES.2014.120141

MLA:

Madhogaria, Satish, et al. "Car detection by fusion of HOG and causal MRF." IEEE Transactions on Aerospace and Electronic Systems 51.1 (2015): 575-590.

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