Seung Hun Jin

A Real-Time Object Tracking System Using a Particle Filter

Particle filters have attracted much attention due to their robust tracking performance in cluttered environments. Particle filters maintain multiple hypotheses simultaneously and use a probabilistic motion model to predict the position of the moving object, and this constitutes a bottleneck to the use of particle filtering in real-time systems due to the expensive computations required. In order to track moving objects in real-time without delay and loss of image sequences, a particle filter algorithm specifically designed for a circuit and the circuit of the object tracking algorithm using the particle filter are proposed. This circuit is designed by VHDL (VHSIC hardware description language), and implemented in an FPGA (field programmable gate array). All of the functions of the proposed particle filter used to track moving objects are implemented in the FPGA. The object tracking system employing this circuit is implemented and then its performance is measured

FPGA-based real-time visual tracking system using adaptive color histograms

Visual tracking using a color feature is based on pattern matching algorithms where the appearance of the target is compared with a reference model in successive images and the position of the target is estimated. The major drawback of these methods is that such operations are usually considered at the top level of image processing both due to the datas intrinsic complexity and to the high computational cost associated with a solution in real time. The probabilistic tracking methods have been shown to be robust and versatile for a modest computational cost. However, the probabilistic tracking methods break down easily when the object moves very fast because these methods search only the regions of interest (ROIs) based on the probability density function (pdf) to estimate the position of the moving object. In this paper, we propose a real-time visual tracking circuit using adaptive color histograms. We propose a window- based image processing structure to improve the processing speed of the visual tracking circuit. The visual tracking circuit searches all regions of the image to perform a matching operation in order to estimate the position of the moving object. The main results of our work are that we have designed and implemented a physically feasible hardware circuit to improve the processing speed of the operations required for real-time visual tracking. Therefore, this work has resulted in the development of a real-time visual tracking system employing an FPGA (field programmable gate array) implemented circuit designed by VHDL (the VHSIC hardware description language). Its performance has been measured to compare with the equivalent software implementation.

Multiple Objects Tracking Circuit using Particle Filters with Multiple Features

Object tracking is a challenging problem in a number of computer vision applications. A number of approaches have been proposed and implemented to track moving objects in image sequences. The particle filter, which recursively constructs the posterior probability distributions of the state space, is the most popular approach. In the particle filter, many kinds of features are used for tracking a moving object in cluttered environments. The specific feature for tracking is selected according to the type of moving object and condition of the tracking environment. Improved tracking performance is obtained by using multiple features concurrently. This paper proposes the particle filter algorithm, using multiple features, such as IFD (inter-frame difference) and gray level, to track a moving object. The IFD is used to detect an object and the gray level is used to distinguish the target object from other objects. This paper designs the circuit of the proposed algorithm using VHDL (VHSIC hardware description language) in an FPGA (field programmable gate array) for tracking without considerable computational cost, since the particle filter requests many computing powers to track objects in real-time. All functions of the proposed tracking system are implemented in an FPGA. A tracking system with this FPGA is implemented and the corresponding performance is measured

FPGA based connected component labeling

Connected component labeling is very useful for separating object and background, and counting objects. The sequential processing architecture proposed by Von Neumann has limits in real-time processing when large data is treated. In this study, a connected component labeling system using parallel hardware architecture is implemented. This system is able to calculate over 200 frames per second (fps) and is labeled a maximum of 255 components. This is a stand-alone system that can receive input image data from a camera and display the resulting image through a monitor.

Object Tracking Circuit using Particle Filter with Multiple Features

Object tracking is a challenging problem in a number of computer vision applications. A number of approaches have been proposed and implemented to track moving objects in image sequences. The particle filter, which recursively constructs the posterior probability distributions of the state space, is the most popular approach. In the particle filter, many kinds of features are used for tracking a moving object in cluttered environments. The specific feature for tracking is selected according to the type of moving object and condition of the tracking environment. Improved tracking performance is obtained by using multiple features concurrently. This paper proposes the particle filter algorithm, using multiple features, such as IFD (inter-fame difference) and gray level, to track a moving object. The IFD is used to detect an object and the gray level is used to distinguish the target object from other objects. This paper designs the circuit of the proposed algorithm using VHDL (VHSIC hardware description language) in an FPGA (field programmable gate array) for tracking without considerable computational cost, since the particle filter requests many computing powers to track objects in real-time. All functions of the proposed tracking system are implemented in an FPGA. A tracking system with this FPGA is implemented and the corresponding performance is measured

Performance Evaluation of Transmitting Images to a PDA through a Wireless Network

This paper presents the performance of transmitting images from a desktop computer to a PDA through a wireless network. Raw images were captured by a digital camera. These raw images were sub-sampled by a frame grabber or compressed in JPEG format by software before sending to the PDA. We implemented the hardware with an FPGA for sub-sampling. For generating a diverse image test bucket for compression we implemented some commonly used filters such as an average filter and median filter in the hardware. The filters affected the size of the compressed images in the JPEG format. The results show that image compression and image sub-sampling improve the performance in the transmission of images

A real-time finite line detection system based on FPGA

Image processing and analysis are active research topics. An intelligent vehicle and a service robot require these techniques. In particular, there is a big demand for line detection because it has a wide range of applications. The line features in an image are used for object identification, robot navigation, and intelligent vehicle control. To detect the lines, a Hough transform is generally used. The Hough transform has good detection results and it is robust to noise, but it takes a long time to execute and it requires a great deal of memory to store the parameter space. This paper proposes a dedicated line detection hardware system. To increase the processing speed, it has a parallel Hough transform unit, and it partitions the parameter space to decrease the memory requirements. It can detect not only the line parameters, but also the exact start and end points of each line, and it sorts these lines by length. It can display the detected line on a monitor via the DVI interface. This system is designed with VHDL and implemented on an XC4VLX200 FPGA. The device usage is about 15% and the maximum clock frequency is 67 MHz. It can detect up to 256 lines in one image frame and it can process up to 149 frames per second. The simulation and real experimental results are given to verify the system performance.

Finger extraction from scene with grayscale morphology and BLOB analysis

In this paper, we describe a method to detect human fingers from images captured by a stereo camera. The images captured by the stereo camera are preprocessed by a skin detection module. Two hands are extracted from the skin regions using disparity information. We then apply grayscale morphology and use BLOB analysis to detect fingers and their directions. The method is simple yet powerful for finger detection. This extraction result can be used for posture and gesture recognition tasks in computer vision.

A real-time color feature tracking system using color histograms

Color feature tracking is based on pattern matching algorithms where the appearance of the target is compared with a reference model in successive images and the position of the target is estimated. The major drawback of these methods is that such operations require expensive computation power. It is bottleneck to implement real-time color feature tracking system. The probabilistic tracking methods have been shown to be robust and versatile for a modest computational cost. However, the probabilistic tracking methods break down easily when the object moves very fast because these methods search only the regions of interest based on the probability density function to estimate the position of the moving object. In this paper, we propose a real-time color feature tracking circuit. We propose a window-based image processing structure to improve the processing speed of the tracking circuit. The tracking circuit searches all regions of the image to perform a matching operation in order to estimate the position of the moving object. The main results of our work are that we have designed and implemented a physically feasible hardware circuit to improve the processing speed of the operations required for real-time color feature tracking. Therefore, this work has resulted in the development of a real-time color feature tracking system employing an FPGA (field programmable gate array) implemented circuit designed by VHDL (the VHSIC hardware description tanguage). Its performance has been measured to compare with the equivalent software implementation.

Dual hand extraction using skin color and stereo information

Extracting the positions of hands is an important step in Human Computer Interaction and Robot Vision applications. Posture and gesture can be extracted from hand positions and the appropriate task can be performed. In this paper, we propose an approach to extract hand images using skin color and stereo information. Our method does not require clear hand-size or high-quality disparity. With a sound training database and an adequate working environment, we obtain nearly 100 percent accuracy. The run time, ignoring calculation of disparity generation time, is also acceptable.