Arablouei et al., 2023 - Google Patents
Animal behavior classification via deep learning on embedded systemsArablouei et al., 2023
View HTML- Document ID
- 5915658424921170812
- Author
- Arablouei R
- Wang L
- Currie L
- Yates J
- Alvarenga F
- Bishop-Hurley G
- Publication year
- Publication venue
- Computers and Electronics in Agriculture
External Links
Snippet
We develop an end-to-end deep-neural-network-based algorithm for classifying animal behavior using accelerometry data on the embedded system of an artificial intelligence of things (AIoT) device installed in a wearable collar tag. The proposed algorithm jointly …
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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of 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
- G06K9/6267—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
- G06K9/6217—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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- 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
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Arablouei et al. | Animal behavior classification via deep learning on embedded systems | |
Arablouei et al. | In-situ classification of cattle behavior using accelerometry data | |
Yang et al. | Classification of broiler behaviours using triaxial accelerometer and machine learning | |
Andriamandroso et al. | Development of an open-source algorithm based on inertial measurement units (IMU) of a smartphone to detect cattle grass intake and ruminating behaviors | |
González et al. | Behavioral classification of data from collars containing motion sensors in grazing cattle | |
Nathan et al. | Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures | |
Riaboff et al. | Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data | |
Wang et al. | Development and validation of an ensemble classifier for real-time recognition of cow behavior patterns from accelerometer data and location data | |
Chelotti et al. | An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle | |
Roberts et al. | Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data | |
Hussain et al. | Activity detection for the wellbeing of dogs using wearable sensors based on deep learning | |
Tian et al. | Real-time behavioral recognition in dairy cows based on geomagnetism and acceleration information | |
Williams et al. | Variable segmentation and ensemble classifiers for predicting dairy cow behaviour | |
Shakeel et al. | A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming | |
Arablouei et al. | Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data | |
Zamansky et al. | Analysis of dogs’ sleep patterns using convolutional neural networks | |
Singh et al. | A novel deep learning approach for arrhythmia prediction on ECG classification using recurrent CNN with GWO | |
Kleanthous et al. | Feature extraction and random forest to identify sheep behavior from accelerometer data | |
Simanungkalit et al. | Use of an ear-tag accelerometer and a radio-frequency identification (RFID) system for monitoring the licking behaviour in grazing cattle | |
KR20200059445A (en) | Method and apparatus for detecting behavior pattern of livestock using acceleration sensor | |
Kumar et al. | Secure and sustainable framework for cattle recognition using wireless multimedia networks and machine learning techniques | |
Bergman et al. | Biometric identification of dairy cows via real-time facial recognition | |
Gill et al. | Human action detection using EfficientNetB3 model | |
Rautiainen et al. | Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data | |
Phung Cong Phi et al. | Classification of cow’s behaviors based on 3-DoF accelerations from cow’s movements |