Mid Sweden University

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.