[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhang et al., 2018 - Google Patents

MiniTracker: a lightweight CNN-based system for visual object tracking on embedded device

Zhang et al., 2018

Document ID
17438879349149462598
Author
Zhang B
Li X
Han J
Zeng X
Publication year
Publication venue
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)

External Links

Snippet

Visual object tracking (VOT) is a computer vision application and has a wide range of use. However, related state of the art algorithms using deep learning methods, are computationally intensive and storage explosive. Whats more, despite many deep learning …
Continue reading at ieeexplore.ieee.org (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
    • G06K9/36—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
    • 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
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • 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
    • G06K9/00006—Acquiring or recognising fingerprints or palmprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored programme computers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application

Similar Documents

Publication Publication Date Title
US11734561B2 (en) Video tracking with deep Siamese networks and Bayesian optimization
Marchisio et al. Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
Cai et al. Yolobile: Real-time object detection on mobile devices via compression-compilation co-design
Li et al. A high performance FPGA-based accelerator for large-scale convolutional neural networks
Dass et al. Vitality: Unifying low-rank and sparse approximation for vision transformer acceleration with a linear taylor attention
Chen et al. Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution
Gao et al. Novel FPGA based Haar classifier face detection algorithm acceleration
He et al. Filter pruning by switching to neighboring CNNs with good attributes
US20210256385A1 (en) Computer-implemented methods and systems for dnn weight pruning for real-time execution on mobile devices
WO2021179281A1 (en) Optimizing low precision inference models for deployment of deep neural networks
Chen et al. A learning framework for n-bit quantized neural networks toward FPGAs
Zhang et al. MiniTracker: a lightweight CNN-based system for visual object tracking on embedded device
Gope et al. Ternary hybrid neural-tree networks for highly constrained iot applications
CN108304925B (en) A pooled computing device and method
De Vita et al. Porting deep neural networks on the edge via dynamic K-means compression: A case study of plant disease detection
Deng et al. Reduced-precision memory value approximation for deep learning
CN113836804A (en) Animal identification model establishing method based on convolutional neural network and application system thereof
Fariselli et al. Integer-only approximated MFCC for ultra-low power audio NN processing on multi-core MCUs
Jeziorek et al. Embedded graph convolutional networks for real-time event data processing on soc fpgas
Voss et al. Convolutional neural networks on dataflow engines
Guo et al. BOOST: block minifloat-based on-device CNN training accelerator with transfer learning
Zhang et al. Speeding-up and compression convolutional neural networks by low-rank decomposition without fine-tuning
Zhang et al. Training deep neural networks with joint quantization and pruning of weights and activations
Mocerino et al. CoopNet: Cooperative convolutional neural network for low-power MCUs
Wang et al. A retrospective evaluation of energy-efficient object detection solutions on embedded devices