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Found 26 papers, 6 papers with code
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision
Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras).
Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras
Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high dynamic range, low latency, and resistance to image blur.
High-definition event frame generation using SoC FPGA devices
In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD -1280 x 720 pixels) onto the image plane in FPGA devices.
Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA
Perception and control systems for autonomous vehicles are an active area of scientific and industrial research.
Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms
Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones.
Detection-segmentation convolutional neural network for autonomous vehicle perception
This architecture is a good solution for embedded perception systems for autonomous vehicles.
Pedestrian detection with high-resolution event camera
Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges.
Real-time FPGA implementation of the Semi-Global Matching stereo vision algorithm for a 4K/UHD video stream
In this paper, we propose a real-time FPGA implementation of the Semi-Global Matching (SGM) stereo vision algorithm.
Traffic sign detection and recognition using event camera image reconstruction
This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera.
Fast-moving object counting with an event camera
The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted.
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection
We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency.
Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e. g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e. g., quantisation, pruning, or dedicated lightweight architectures.
Traffic Sign Classification Using Deep and Quantum Neural Networks
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision.
Traffic Sign Detection With Event Cameras and DCNN
The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89. 9% mAP@0. 5.
Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA
The AirSim simulator running on a PC and an Arty Z7 development board with a Zynq SoC chip from AMD Xilinx were used.
Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm
Neuromorphic vision is a rapidly growing field with numerous applications in the perception systems of autonomous vehicles.
Towards real-time and energy efficient Siamese tracking -- a hardware-software approach
To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA).
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream
Object detection is an essential component of many vision systems.
Training dataset generation for bridge game registration
This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection.
A Connected Component Labelling algorithm for multi-pixel per clock cycle video strea
This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic.
A simple vision-based navigation and control strategy for autonomous drone racing
In this paper, we present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags.
Exploration of Hardware Acceleration Methods for an XNOR Traffic Signs Classifier
Deep learning algorithms are a key component of many state-of-the-art vision systems, especially as Convolutional Neural Networks (CNN) outperform most solutions in the sense of accuracy.
Optimisation of the PointPillars network for 3D object detection in point clouds
In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud.
Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking
In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented.
Vision based hardware-software real-time control system for autonomous landing of an UAV
In this paper we present a vision based hardware-software control system enabling autonomous landing of a multirotor unmanned aerial vehicle (UAV).
Foreground object segmentation in RGB-D data implemented on GPU
This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support.
