Object detection in large-scale real-world scenes requires efficient multi-class detection approaches. Random forests have been shown to handle large training datasets and many classes for object detection efficiently. The most prominent example is the commercial application of random forests for gaming [37]. In this paper, we describe the general framework of random forests for multi-class object detection in images and give an overview of recent developments and implementation details that are relevant for practitioners. © 2012 Springer-Verlag.
CITATION STYLE
Gall, J., Razavi, N., & Van Gool, L. (2012). An introduction to random forests for multi-class object detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7474 LNCS, pp. 243–263). https://doi.org/10.1007/978-3-642-34091-8_11
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