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Journal of Real-time Image …, 2008
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, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, manmachine communications and intelligent environments.
A human action recognition system for embedded computer vision application
Computer Vision and Pattern …, 2007
In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear Support Vector Machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known Motion History Image (MHI) and propose a new Hierarchical Motion History Histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.
Embedded system for real-time human motion detection
2010 2nd International Conference on Image Processing Theory, Tools and Applications, 2010
This paper describes an embedded system for real-time human motion detection using a fixed camera. A modified version of the Codebook algorithm is developed to detect moving objects. This algorithm provides fast background modelling and subtraction with small storage memory requirements. Then, the system detects humans using a simplified Skeletonization algorithm, which uses the individual human shape and does not need a model comparison. Functional and timing simulations are applied by using MATLAB and Visual Studio on PC. Finally, the system is installed on ALTERA Cyclone™ II DSP development board and implemented using the Nios II processor and some hardware accelerators.
Recognizing human actions based on motion information and SVM
2006
In this paper, we propose a new system for human action recognition with a view to applications in security systems, man-machine communications and intelligent environments. Our system is based on very simple features in order to achieve high-speed recognition in real-world applications. We have chosen three main techniques to build a system that can work in real-time. Firstly, we choose Motion History Images and related features. Secondly, we use a template matching methods instead of state-space methods that need expensive modelling processes; finally, we use linear classifier support vector machine (SVM) for fast classification. Experimental results show that this system can achieve good performance in human action recognition in realtime embedded applications, such as intelligent environments.
Motion history histograms for human action recognition
Embedded Computer Vision, 2009
In this chapter, a compact human action recognition system is presented with a view to applications in security systems, human-computer interaction, and intelligent environments. There are three main contributions: Firstly, the framework of an embedded human action recognition system based on a support vector machine (SVM) classifier and some compact motion features has been presented. Secondly, the limitations of the well-known motion history image (MHI) are addressed and a new motion history histograms (MHH) feature is introduced to represent the motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. We combine MHI and MHH into a low-dimensional feature vector for the system and achieve improved performance in human action recognition over comparable methods that use tracking-free temporal template motion representations. Finally, a simple system based on SVM and MHI has been implemented on a reconfigurable embedded computer vision architecture for real-time gesture recognition.
A Real-Time Implementation of Moving Object Action Recognition System Based on Motion Analysis
This paper proposes a PixelStreams-based FPGA implementation of a real-time system that can detect and recognize human activity using Handel-C. In the first part of our work, we propose a GUI programmed using Visual C++ to facilitate the implementation for novice users. Using this GUI, the user can program/erase the FPGA or change the parameters of different algorithms and filters. The second part of this work details the hardware implementation of a real-time video surveillance system on an FPGA, including all the stages, i.e., capture, processing, and display, using DK IDE. The targeted circuit is an XC2V1000 FPGA embedded on Agility's RC200E board. The PixelStreams-based implementation was successfully realized and validated for real-time motion detection and recognition.
Low-Cost Real-Time 2-D Motion Detection Based on Reconfigurable Computing
IEEE Transactions on Instrumentation and Measurement, 2000
This paper presents a method for real-time hand motion detection in two-dimensional (2-D) space. A new input device for kinetically challenged persons that uses this method is presented. The device consists of a solid-state accelerometer that senses 2-D motion, a microcontroller that samples the data in real time, and an embedded field-programmable gate array (FPGA) device that distinguishes the types of motion from programmable motion vocabularies. The system has a quadratic capability O(n 2 ) in detecting motions, while the hardware used has a linearcomplexity O(n). The motion-detection computational model is presented, along with experimental results. The system adaptation to individual requirements and the cost versus quality tradeoff can be addressed through reconfiguration.