Moving Object Tracking Application: Fpga And Model Based Implementation Using Image Processing Algorithms. Abstract— With increased resource size, powerful DSP blocks and large on-chip memory, Field Programmable Gate Array (FPGA) devices play a major role as hardware platforms for implementing compute intensive video image processing applications. In this paper, image processing algorithms are used for tracking a moving video object. The image processing algorithms used are (a) Noisy video generation with random motion (b) Video image median filter (c) Video image back ground removal (d) Video image thresholding (e) Video image edge detection (f) Video image height and width calculation (g) Video image center computation (h) Video image and center image overlay. The image processing algorithms are developed initially by Model Based Design Approach using Simulink models of MATHWORK’s MATLAB Tool. Then these algorithms are implemented on ALTERA CYCLONE-II FPGA device using TERASIC DE2 FPGA hardware kit and ALTERA QUARTUS-II software tool. The input video image is taken from a NTSC/PAL camera and processed in real time using the algorithms on the FPGA and the resulted tracked video image output is displayed on a VGA monitor. Keywords—fpga; simulink; video image processing; tracking I. INTRODUCTION Moving object tracking is one of the fundamental components of computer vision; it can be very beneficial in applications such as unmanned aerial vehicle, surveillance, automated traffic control, biomedical image analysis, intelligent robots etc.The problem of object tracking is of considerable interest in the scientific community and it is still an open and active field of research. That’s why; this paper is a good step for a beginner to move towards moving object tracking application. Image processing is one of the major applications in embedded domain, which requires high effort in computation. In today’s world most sensing applications require some form of digital signal processing. The two major contenders for signal processing hardware platforms are Digital Signal Processing (DSP) processor and Field Programmable Gate Array (FPGA). DSP processor offers high compute intensive, serial processing for complete System on a Chip (SOC) embedded product development, where as FPGA offers highly flexible, parallel processing for a System on Programmable Chip (SoPC) development for proof of concept at formative stage of the system design, leading to manufacturable prototype at a later stage before the final Application Specific Integrated Chip (ASIC) implementation. The FPGA contains logic components that can be programmed to perform complex mathematical functions making them highly suitable for the implementation of matrix algorithms. Therefore, FPGAs are an ideal choice for implementation of real time image processing algorithms. For a beginner it should be necessary to approach the problem at hand through model based approach using various modeling tools like MATLAB, LABVIEW and SILAB etc. MATLAB offers drag and drop Simulink modules to translate DSP algorithms to logical hardware entities to understand about, how signal and image processing algorithms work. Simulink model implementation is a good step for a learner on their respective domain to work. This paper covers the implementation of image processing algorithms for moving object tracking applications using Simulink model and FPGA. An intermediate stage between Simulink modeling and final FPGA implementation could be System Generator modeling for Xilinx FPGA, to arrive at ready HDL (VHDL or VERILOG) code generation for a Xilinx specific FPGA devices and hardware kits. In present work the Simulink logic entities are translated to image processing modules and introduced into the video chain established on TERASIC DE2 FPGA hardware evaluation kit [7]. The video source is from PAL/NTSC compatible camera and the output display is on 640X480 resolution VGA monitor. The functional implementation of all processes are done using ALTERA QUARTUS-II tool. Section-II describes Related Works, Section-III describes Moving Object Tracking Algorithms, Section-IV describes Implementation of Algorithms, Section-V describes Experimental Results and Section- VI describes Conclusion and Future Scopes. Sofia Nayak Shashank Sekhar Pujari P.G. Department of Embedded System Design Sambalpur University Institute of Information Technology Jyoti Vihar, Burla-768019, Odisha, India. nayaksofia1989@gmail.com pujarishashank@gmail.com 2015 International Conference on Computing Communication Control and Automation 978-1-4799-6892-3/15 $31.00 © 2015 IEEE DOI 10.1109/ICCUBEA.2015.185 932