Evaluating the effect of different distances on the pixels per object and image classification

Abstract: In the last decades camera systems have continuously evolved and have found wide range of applications. One of the main applications of a modern camera system is surveillance in outdoor areas. The camera system, based on local computations, can detect and classify objects autonomously. However, the distance of the objects from the camera plays a vital role on the classification results. This could be specially challenging when lighting conditions are varying. Therefore, in this thesis, we are examining the effect of changing dis-tances on object in terms of number of pixels. In addition, the effect of distance on classification is studied by preparing four different sets. For consideration of high signal to noise ratio, we are integrating thermal and visual image sensors for the same test in order to achieve better spectral resolution. In this study, four different data sets, thermal, visu-al, binary from visual and binary from thermal have been prepared to train the classifier. The categorized objects include bicycle, human and vehicle. Comparative studies have been performed in order to identify the data sets accuracy. It has been demonstrated that for fixed distances bi-level data sets, obtained from visual images, have better accuracy. By using our setup, the object (human) with a length of 179 and width of 30 has been classified correctly with minor error up to 150 meters for thermal, visual as well as binary from visual. Moreover, for bi-level images from thermal, the human object has been correctly classified as far away as 250 meters.