Special issue on real-time image and video processing for pattern recognition systems and applications (original) (raw)

Journal of Real-Time Image Processing manuscript No. (will be inserted by the editor)

2010

FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture Received: date / Revised: date Abstract In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. “motion history image”) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application,...

Real-Time Image Processing Using Graphics Hardware: A Performance Study

Lecture Notes in Computer Science, 2005

Background updating is an important aspect of dynamic scene analysis. Two critical problems: sudden camera perturbation and the sleeping person problem, which arise frequently in real-world surveillance and monitoring systems, are addressed in the proposed scheme. The paper presents a multi-color model where multiple color clusters are used to represent the background at each pixel location. In the proposed background updating scheme, the updates to the mean and variance of each color cluster at each pixel location incorporate the most recently observed color values. Each cluster is assigned a weight which measures the time duration and temporal recurrence frequency of the cluster. The sleeping person problem is tackled by virtue of the observation that at a given pixel location, the time durations and recurrence frequencies of the color clusters representing temporarily static objects are smaller compared to those of color clusters representing the true background colors when measured over a sufficiently long history. The camera perturbation problem is solved using a fast camera motion detection algorithm, allowing the current background image to be registered with the background model maintained in memory. The background updating scheme is shown to be robust even when the scene is very busy and also computationally efficient, making it suitable for real-time surveillance and monitoring systems. Experimental results on real traffic monitoring and surveillance videos are presented.

Real-Time Image and Video Processing: From Research to Reality

Synthesis Lectures on Image, Video, and Multimedia Processing, 2006

This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, the readers are exposed to a wide variety of techniques and tools, which they can then employ for designing a real-time image or video processing system of interest. KEYWORDS Real-time image and video processing, Real-time implementation strategies, Algorithmic simplifications for real-time image and video processing, Hardware platforms for real-time image and video processing, Software methods for real-time image and video processing v

On the Real Time Object Detection and Tracking

JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH

Object detection and tracking is widely used for detecting motions of objects present in images and video.Since last so many decades, numerous real time object detection and tracking methods have been proposed byresearchers. The proposed methods for objects to be tracked till date require some preceding informationassociated with moving objects. In real time object detection and tracking approach segmentation is the initialtask followed by background modeling for the extraction of predefined information including shape of the objects,position in the starting frame, texture, geometry and so on for further processing of the cluster pixels and videosequence of these objects. The object detection and tracking can be applied in the fields like computerized videosurveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, militarysurveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. In thispaper we focus...

REAL-TIME RECOGNITION AND TRACKING OF MOVING OBJECTS

A multi-camera monitoring system for online recognition of moving objects is considered. The system consists of several autonomous vision subsystems. Each of them is able to monitor an area of interest with the aim to reveal and recognize characteristic patterns and to track the motion of the selected configuration. Each subsystem recognizes the existence of the predefined objects in order to report expected motion while automatically tracking the selected object. Simultaneous tracking by two or more cameras is used to measure the instant distance of the tracked object. A modular conception enables simple extension by several static and mobile cameras mechanically oriented in space by the pan and tilt heads. The open architecture of the system allows additional subsystems integration and the day and night image processing algorithms extension.

A Survey on Real Time Object Detection, Tracking and Recognition in Image Processing

International Journal of Computer Applications, 2014

Object detection, tracking and recognition in real time is a very essential task in computer vision. There are lots of research work have been done in this area. Yet it needs to be accuracy in recognizing object. The most objective of this review is to present an overview of the approaches used and also the challenges involved. In this paper we concentrate on different object detection methods, tracking and recognition methods are discussed.

A Study on Computer Vision Systems for Real-Time Object Detection and Tracking

International Journal of Computer Applications, 2017

Computer Vision (CV) concentrates on the automatic extraction, examination and comprehension of valuable data from a solitary image or a group of images. Object tracking, one of the key areas in CV has received a lot of attenstion in recent times. Tracking objects is a systematic process of monitoring the movement of a target object from its initial state to the nth state over a period of time using a camera. This technique is usually employed as a security feature in both military and civilian systems. However, prior studies has shown that tracking objects in motion is a very difficult task and is a hot research hotspot in the field of computer vision and machine learning. In this review paper we discuess various techniques in detection, tracking and some other related works of moving objects in video streams.

Real Time Architectures for Moving-Objects Tracking

2007

The problem of object tracking is of considerable interest in the scientific community and it is still an open and active field of research. In this paper we address the comparison of two different specific purpose architectures for object tracking based on motion and colour segmentation. On one hand, we have developed a new multi-object segmentation device based on an existing optical flow estimation system. This architecture allows video tracking of fast moving objects based on high speed acquisition cameras. On the other hand, the second approach consists on real time filtering of chromatic components. Multi-object tracking is performed based on segmentation of pixel neighbourhoods according to a predefined colour. In this contribution we evaluate the two methods, comparing their performance, resource consumption and finally, we discuss which architecture fits better in different working cenarios.