82,714 research outputs found
Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device
Currently, most designers face a daunting task to
research different design flows and learn the intricacies of
specific software from various manufacturers in
hardware/software co-design. An urgent need of creating a
scalable hardware/software co-design platform has become a key
strategic element for developing hardware/software integrated
systems. In this paper, we propose a new design flow for building
a scalable co-design platform on FPGA-based system-on-chip.
We employ an integrated approach to implement a histogram
oriented gradients (HOG) and a support vector machine (SVM)
classification on a programmable device for pedestrian tracking.
Not only was hardware resource analysis reported, but the
precision and success rates of pedestrian tracking on nine open
access image data sets are also analysed. Finally, our proposed
design flow can be used for any real-time image processingrelated
products on programmable ZYNQ-based embedded
systems, which benefits from a reduced design time and provide a
scalable solution for embedded image processing products
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Retinal ganglion cell software and FPGA model implementation for object detection and tracking
This paper describes the software and FPGA
implementation of a Retinal Ganglion Cell model which detects
moving objects. It is shown how this processing, in conjunction
with a Dynamic Vision Sensor as its input, can be used to
extrapolate information about object position. Software-wise, a
system based on an array of these of RGCs has been developed in
order to obtain up to two trackers. These can track objects in a
scene, from a still observer, and get inhibited when saccadic
camera motion happens. The entire processing takes on average
1000 ns/event. A simplified version of this mechanism, with a mean
latency of 330 ns/event, at 50 MHz, has also been implemented in
a Spartan6 FPGA.European Commission FP7-ICT-600954Ministerio de EconomĂa y Competitividad TEC2012-37868-C04-02Junta de AndalucĂa P12-TIC-130
- âŠ