Xuan Dai Pham

FPGA Design and Implementation of a Real-Time Stereo Vision System

Stereo vision is a well-known ranging method because it resembles the basic mechanism of the human eye. However, the computational complexity and large amount of data access make real-time processing of stereo vision challenging because of the inherent instruction cycle delay within conventional computers. In order to solve this problem, the past 20 years of research have focused on the use of dedicated hardware architecture for stereo vision. This paper proposes a fully pipelined stereo vision system providing a dense disparity image with additional sub-pixel accuracy in real-time. The entire stereo vision process, such as rectification, stereo matching, and post-processing, is realized using a single field programmable gate array (FPGA) without the necessity of any external devices. The hardware implementation is more than 230 times faster when compared to a software program operating on a conventional computer, and shows stronger performance over previous hardware-related studies.

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

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

Background compensation using Hough transformation

This paper proposes a method for detecting the camera motion between two successive images to compensate background motion. The camera motion is restricted with regard to panning, tilting, and zooming. For small panning and tilting angles and small values of zooming difference, we assume that the apparent background motion occurring between two consecutive images can be approximated. We perform this using a scaling transformation followed by a translation. The vertical and horizontal histograms of two successive images are created and then matched using Hough transformations. The transformation parameters are determined when the vertical and horizontal histograms are matched. A multi-resolution Hough transformation is employed to reduce processing time.

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.

Automatic exposure compensation for line detection applications

The detection of lines in an image is an important task. In spite of the numerous research papers that have been published on line extraction, there is a lack of real-world applications relating to coping with illumination changes in scenes. This paper provides an automatic exposure compensation scheme that transforms images under unknown illumination conditions into images that can be used as the best preprocessing data for line detection. Our method is tested on gray level frames captured from the camera where the exposure parameter is changed continuously. Among these frames, the image with the best contrast is selected based on the image entropy. We then apply contrast stretching to transform this poorly illuminated image into one that has better visibility. Afterwards, the Canny edge detection algorithm is applied to obtain the input for the standard Hough transform, which is the line extraction algorithm. Furthermore, our system detects lines in real-time, so it is suitable for many real-world applications.