AbstractsEngineering

Image processing emerges from the curiosity of human vision. To translate, what we see in everyday life and how we differentiate between objects, to robotic vision is a challenging and modern research topic. This thesis focuses on detecting a pedestrian within a standard format of an image. The efficiency of the algorithm is observed after its implementation in FPGA. The algorithm for pedestrian detection was developed using MATLAB as a base. To detect a pedestrian, a histogram of oriented gradient (HOG) of an image was computed. Study indicates that HOG is unique for different objects within an image. The HOG of a series of images was computed to train a binary classifier. A new image was then fed to the classifier in order to test its efficiency. Within the time frame of the thesis, the algorithm was partially translated to a hardware description using VHDL as a base descriptor. The proficiency of the hardware implementation was noted and the result exported to MATLAB for further processing. A hybrid model was created, in which the pre-processing steps were computed in FPGA and a classification performed in MATLAB. The outcome of the thesis shows that HOG is a very efficient and effective way to classify and differentiate different objects within an image. Given its efficiency, this algorithm may even be extended to video.