On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of moving humans using eigen-features and support vector machines
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
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Feature subset selection has received considerable attention in the machine learning literature, however, it has not been fully explored or exploited in the computer vision area. In this paper, we consider the problem of object detection using Genetic Algorithms (GAs) for feature subset selection. We argue that feature selection is an important problem in object detection, and demonstrate that GAs provide a simple, general, and powerful framework for selecting good sets of features, leading to lower detection error rates. As a case study, we have chosen to perform feature extraction using the popular method of Principal ComponentAnalysis (PCA) and classi.cation using Support Vector Machines (SVMs). We have tested this framework on two difficult and practical object detection problems: vehicle detection and face detection. Experimental results demonstrate significant performance improvements in both cases.