HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture

• URL: http://arxiv.org/abs/2109.04119v1
• Date: Thu, 9 Sep 2021 09:15:56 GMT
• Title: HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture
• Authors: Pedro Machado, Andreas Oikonomou, Joao Filipe Ferreira, T.M. McGinnity
• Abstract summary: Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. HSMD algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) Results show that the HSMD was ranked overall first among the competing approaches.
• Score: 0.2580765958706854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection of moving objects is a trivial task performed by vertebrate retinas, yet a complex computer vision task. Object-motion-sensitive ganglion cells (OMS-GC) are specialised cells in the retina that sense moving objects. OMS-GC take as input continuous signals and produce spike patterns as output, that are transmitted to the Visual Cortex via the optic nerve. The Hybrid Sensitive Motion Detector (HSMD) algorithm proposed in this work enhances the GSOC dynamic background subtraction (DBS) algorithm with a customised 3-layer spiking neural network (SNN) that outputs spiking responses akin to the OMS-GC. The algorithm was compared against existing background subtraction (BS) approaches, available on the OpenCV library, specifically on the 2012 change detection (CDnet2012) and the 2014 change detection (CDnet2014) benchmark datasets. The results show that the HSMD was ranked overall first among the competing approaches and has performed better than all the other algorithms on four of the categories across all the eight test metrics. Furthermore, the HSMD proposed in this paper is the first to use an SNN to enhance an existing state of the art DBS (GSOC) algorithm and the results demonstrate that the SNN provides near real-time performance in realistic applications.

Related papers

• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion [54.668445421149364]
  Deep learning-based hyperspectral image (HSI) super-resolution aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs) In this letter, we propose a novel adversarial automatic data augmentation framework ADASR that automatically optimize and augments HSI-MSI sample pairs to enrich data diversity for HSI-MSI fusion.
  arXiv  Detail & Related papers  (2023-10-11T07:30:37Z)
• Computational models of object motion detectors accelerated using FPGA technology [0.3626013617212667]
  This PhD research introduces three key contributions in the domain of object motion detection. MHSNN: A specialized four-layer Spiking Neural Network architecture inspired by vertebrate retinas. HSMD: Enhancing Dynamic Background Subtraction (DBS) using a tailored three-layer SNN. NeuroHSMD: Building upon HSMD, this adaptation implemented the SNN component on dedicated hardware.
  arXiv  Detail & Related papers  (2023-08-23T20:26:12Z)
• IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [88.35145788575348]
  Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing. The lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications. We construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets.
  arXiv  Detail & Related papers  (2023-01-31T01:24:45Z)
• Detecting Algorithmically Generated Domains Using a GCNN-LSTM Hybrid Neural Network [10.617124610646488]
  Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel. AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In this paper, a GCNN (gated convolutional neural network)-LSTM (long short-term memory) Hybrid Neural Network (GLHNN) for AGD detection is proposed.
  arXiv  Detail & Related papers  (2022-08-06T05:15:45Z)
• Effective classification of ecg signals using enhanced convolutional neural network in iot [0.0]
  This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL) Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions. The results show that the proposed strategy outperforms others in terms of classification accuracy.
  arXiv  Detail & Related papers  (2022-02-08T13:37:23Z)
• NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector [2.0625936401496237]
  Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects. The detection of object motion is done by specialised retinal ganglion cells named Object-motion-sensitive ganglion cells (OMS-GC) The Neuromorphic Hybrid Spiking Motion Detector (NeuroHSMD) proposed in this work accelerates the HSMD algorithm using Field-Programmable Gate Arrays (FPGAs) The results show that the NeuroHSMD has produced the same results as the HSMD algorithm in real-time without degradation of quality.
  arXiv  Detail & Related papers  (2021-12-12T00:01:15Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds [51.47100091540298]
  We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks. CPFN improves the state-of-the-art SPFN performance by 13-14% on high-resolution point cloud datasets and specifically improves the detection of fine-scale primitives by 20-22%.
  arXiv  Detail & Related papers  (2021-08-31T23:27:33Z)
• A SAR speckle filter based on Residual Convolutional Neural Networks [68.8204255655161]
  This work aims to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs) The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
  arXiv  Detail & Related papers  (2021-04-19T14:43:07Z)
• Urban Change Detection by Fully Convolutional Siamese Concatenate Network with Attention [0.6999740786886537]
  Change detection (CD) is an important problem in remote sensing, especially in disaster time for urban management. Object-based models are preferred to pixel-based methods for handling very high-resolution remote sensing (VHR RS) images. In this paper, a fully automatic change-detection algorithm on VHR RS images is proposed that deploys Fully Convolutional Siamese Concatenate networks.
  arXiv  Detail & Related papers  (2021-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.