A weighted image reconstruction based on PCA for pedestrian detection

Pedestrian detection is a task usually associated with security and surveillance systems. The development of a pedestrian detection system poses a hard challenge, because of its inherently complex nature. In this work, we present an analysis of an existing pedestrian detection model based on PCA reconstruction errors. We investigate how the method works and where changes can be made to improve its original performance. The proposed improvements enhance the system´s accuracy by using weights, that are found in an automated way using a genetic algorithm. We also found that some reconstruction errors used by the original method are not strictly necessary and therefore they can be eliminated to reduce the classifying time by half.