Nothing Special   »   [go: up one dir, main page]

Wang et al., 2018 - Google Patents

An adaptive particle swarm optimization for underwater target tracking in forward looking sonar image sequences

Wang et al., 2018

View PDF
Document ID
5717485440302618485
Author
Wang X
Wang G
Wu Y
Publication year
Publication venue
IEEE Access

External Links

Snippet

In order to obtain accurate underwater target tracking results in forward-looking sonar image sequences, an adaptive particle swarm optimization (APSO) algorithm is proposed to track the underwater target. Specifically, an adaptive inertia weight is first applied to solve the …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Similar Documents

Publication Publication Date Title
Wang et al. An adaptive particle swarm optimization for underwater target tracking in forward looking sonar image sequences
Kraus et al. Uncertainty estimation in one-stage object detection
Zhang et al. Visual SLAM for underwater vehicles: A survey
Guth et al. Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach
Lee et al. SLAM with single cluster PHD filters
Salmond Mixture reduction algorithms for point and extended object tracking in clutter
Hu et al. Density-insensitive unsupervised domain adaption on 3d object detection
Azzam et al. A stacked LSTM-based approach for reducing semantic pose estimation error
Leung et al. An improved weighting strategy for rao-blackwellized probability hypothesis density simultaneous localization and mapping
Alla et al. Vision-based Deep Learning algorithm for Underwater Object Detection and Tracking
CN116528171A (en) Mobile sensor network target tracking method based on force guiding positioning
Chame et al. Neural network for black-box fusion of underwater robot localization under unmodeled noise
Joubert et al. Pose uncertainty in occupancy grids through monte carlo integration
Zheng et al. Research on real time obstacle avoidance method for AUV based on combination of ViT-DPFN and MPC
Al Said et al. Retracted: An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development
Wang et al. Deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle
Jiang et al. Underwater loop-closure detection for mechanical scanning imaging sonar by filtering the similarity matrix with probability hypothesis density filter
Chen et al. An obstacle detection method for USV by fusing of radar and motion stereo
Wang et al. Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning
Chen et al. Sparse Gaussian process regression based ground segmentation for autonomous land vehicles
Arnström State estimation for truck and trailer systems using deep learning
Wilts et al. Enhanced state estimation based on particle filter and sensor data with non-gaussian and multimodal noise
Gao et al. More robust object tracking via shape and motion cue integration
Ruan et al. Combining scene coordinate regression and absolute pose regression for visual relocalization
CN114841220A (en) Training method of underwater acoustic target recognition model and underwater acoustic target recognition method