A data-driven detection optimization framework

• Published in: Neurocomputing (Amsterdam) Vol. 104; pp. 35 - 49
• Main Authors: Robson Schwartz, William, Hugo Cunha de Melo, Victor, Pedrini, Helio, Davis, Larry S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15-03-2013; Elsevier
• Subjects: Accuracy; Applied sciences; Artificial intelligence; Channels; Computational efficiency; Computer science; control theory; systems; Detectors; Exact sciences and technology; Human; Human detection; Mathematical models; Object detection; Optimization; Partial least squares; Pattern recognition. Digital image processing. Computational geometry; Regression; Regression models; Regression models; Human detection; Partial least squares; Object detection; Computer vision; Variability; High precision; Model driven architecture; Data driven modelling; Object recognition; Optimization; Experimental result; Regression model; Scene analysis; Data models; Multiple channel; Pattern extraction; Object location
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2012.09.021

Due to the large amount of data to be processed by visual applications aiming at extracting high-level understanding of the scene, low-level methods such as object detection are required to have not only high accuracy but also low computational cost in order to provide fast and reliable information. Training sets containing samples representing multiple scenes are used to learn object detectors that can be reliably used in different scenarios. In general, information extracted from multiple feature channels is combined to capture the large variability present in these different environments. Although this approach provides accurate detection results, it usually leads to a high computational cost. On the other hand, if characteristics of the scene are known before-hand, a set of simple and fast computing features might be sufficient to provide high accuracy at a low computational cost. Therefore, it is valuable to seek a balance between these two extremes such that the detection method not only works well in different scenarios but also is able to extract enough information from a scene. We integrate a set of data-driven regression models with a multi-stage based human detection method trained to be used in different environments. The regressions are used to estimate the detector response at each stage and the location of the objects. The use of the regression models allows the method to reject large number of detection windows quickly. Experimental results based on human detection show that the addition of the regression models reduces the computational cost by as much as ten times with very small or no degradation on detection accuracy.