Evolving cascades of voting feature detectors for vehicle detection in satellite imagery

We propose an evolutionary method for detection of vehicles in satellite imagery which involves a large number of simple elementary features and multiple detectors trained by genetic programming. The complete detection system is composed of several detectors that are chained into a cascade and successively filter out the negative examples. Each detector is a committee of genetic programming trees that together vote over the decision concerning vehicle presence, and is trained only on the examples classified as positive by the previous cascade node. The individual trees use typical arithmetic transformations to aggregate features selected from a very large collections of Haar-like features derived from the input image. The paper presents detailed description of the proposed algorithm and reports the results of an extensive computational experiment carried out on real-world satellite images. The evolved detection system exhibits competitive sensitivity and relatively low false positive rate for testing images, despite not making use of domain-specific knowledge.