Car detection by fusion of HOG and causal MRF

• Published in: IEEE transactions on aerospace and electronic systems Vol. 51; no. 1; pp. 575 - 590
• Main Authors: Madhogaria, Satish, Baggenstoss, Paul M., Schikora, Marek, Koch, Wolfgang, Cremers, Daniel
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; False alarms; Feature extraction; Hidden Markov models; Histograms; Probability density function; Support vector machines; Surveillance; Vectors
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2014.120141

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.