4,231 research outputs found
Accelerated hardware video object segmentation: From foreground detection to connected components labelling
This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency
Binary object recognition system on FPGA with bSOM
Tri-state Self Organizing Map (bSOM), which takes binary inputs and maintains tri-state weights, has been used for classification rather than clustering in this paper. The major contribution here is the demonstration of the potential use of the modified bSOM in security surveillance, as a recognition system on FPGA
A single-chip FPGA implementation of real-time adaptive background model
This paper demonstrates the use of a single-chip
FPGA for the extraction of highly accurate background
models in real-time. The models are based
on 24-bit RGB values and 8-bit grayscale intensity
values. Three background models are presented, all
using a camcorder, single FPGA chip, four blocks
of RAM and a display unit. The architectures have
been implemented and tested using a Panasonic NVDS60B
digital video camera connected to a Celoxica
RC300 Prototyping Platform with a Xilinx Virtex
II XC2v6000 FPGA and 4 banks of onboard RAM.
The novel FPGA architecture presented has the advantages
of minimizing latency and the movement of
large datasets, by conducting time critical processes
on BlockRAM. The systems operate at clock rates
ranging from 57MHz to 65MHz and are capable
of performing pre-processing functions like temporal
low-pass filtering on standard frame size of 640X480
pixels at up to 210 frames per second
Design of an FPGA-based smart camera and its application towards object tracking : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
Smart cameras and hardware image processing are not new concepts, yet despite the fact both have existed several decades, not much literature has been presented on the design and development process of hardware based smart cameras. This thesis will examine and demonstrate the principles needed to develop a smart camera on hardware, based on the experiences from developing an FPGA-based smart camera. The smart camera is applied on a Terasic DE0 FPGA development board, using Terasic’s 5 megapixel GPIO camera. The algorithm operates at 120 frames per second at a resolution of 640x480 by utilising a modular streaming approach. Two case studies will be explored in order to demonstrate the development techniques established in this thesis.
The first case study will develop the global vision system for a robot soccer implementation. The algorithm will identify and calculate the positions and orientations of each robot and the ball. Like many robot soccer implementations each robot has colour patches on top to identify each robot and aid finding its orientation. The ball is comprised of a single solid colour that is completely distinct from the colour patches. Due to the presence of uneven light levels a YUV-like colour space labelled YC1C2 is used in order to make the colour values more light invariant. The colours are then classified using a connected components algorithm to segment the colour patches. The shapes of the classified patches are then used to identify the individual robots, and a CORDIC function is used to calculate the orientation.
The second case study will investigate an improved colour segmentation design. A new HSY colour space is developed by remapping the Cartesian coordinate system from the YC1C2 to a polar coordinate system. This provides improved colour segmentation results by allowing for variations in colour value caused by uneven light patterns and changing light levels
Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications
Compute and memory demands of state-of-the-art deep learning methods are
still a shortcoming that must be addressed to make them useful at IoT
end-nodes. In particular, recent results depict a hopeful prospect for image
processing using Convolutional Neural Netwoks, CNNs, but the gap between
software and hardware implementations is already considerable for IoT and
mobile edge computing applications due to their high power consumption. This
proposal performs low-power and real time deep learning-based multiple object
visual tracking implemented on an NVIDIA Jetson TX2 development kit. It
includes a camera and wireless connection capability and it is battery powered
for mobile and outdoor applications. A collection of representative sequences
captured with the on-board camera, dETRUSC video dataset, is used to exemplify
the performance of the proposed algorithm and to facilitate benchmarking. The
results in terms of power consumption and frame rate demonstrate the
feasibility of deep learning algorithms on embedded platforms although more
effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication.
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