Cattelani et al., 2014 - Google Patents
A particle filtering approach for tracking an unknown number of objects with dynamic relationsCattelani et al., 2014
View PDF- Document ID
- 3862037189610761988
- Author
- Cattelani L
- Manfredotti C
- Messina E
- Publication year
- Publication venue
- Journal of Mathematical Modelling and Algorithms in Operations Research
External Links
Snippet
In recent years there has been a growing interest on particle filters for solving tracking problems, thanks to their applicability to problems with continuous, non-linear and non- Gaussian state spaces, which makes them more suited than hidden Markov models, Kalman …
- 239000002245 particle 0 title abstract description 79
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- 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
- G06K9/62—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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- 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
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Antonini et al. | Behavioral priors for detection and tracking of pedestrians in video sequences | |
Maggio et al. | Efficient multitarget visual tracking using random finite sets | |
Berclaz et al. | Multiple object tracking using k-shortest paths optimization | |
Bera et al. | Realtime multilevel crowd tracking using reciprocal velocity obstacles | |
Black et al. | Multi-camera image measurement and correspondence | |
JP2005165688A (en) | Multiple objects tracking method and system | |
Dore et al. | Bayesian tracking for video analytics | |
Deisenroth et al. | Expectation propagation in Gaussian process dynamical systems | |
De Laet et al. | Shape-based online multitarget tracking and detection for targets causing multiple measurements: Variational Bayesian clustering and lossless data association | |
Choi et al. | Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking | |
Beyer et al. | Towards a principled integration of multi-camera re-identification and tracking through optimal bayes filters | |
Hassan et al. | An adaptive sample count particle filter | |
Makris et al. | A hierarchical feature fusion framework for adaptive visual tracking | |
Cattelani et al. | A particle filtering approach for tracking an unknown number of objects with dynamic relations | |
Ko | A survey on behaviour analysis in video surveillance applications | |
Mittal et al. | Pedestrian detection and tracking using deformable part models and Kalman filtering | |
Jiang et al. | Flow‐assisted visual tracking using event cameras | |
Hoseinnezhad et al. | Consistency of robust estimators in multi-structural visual data segmentation | |
Wu et al. | In situ evaluation of tracking algorithms using time reversed chains | |
Narayana | Automatic tracking of moving objects in video for surveillance applications | |
Garcia et al. | Fuzzy region assignment for visual tracking | |
Miller et al. | Kalman filter-based tracking of multiple similar objects from a moving camera platform | |
Del Blanco et al. | An advanced Bayesian model for the visual tracking of multiple interacting objects | |
Lim et al. | Non-overlapping distributed tracking system utilizing particle filter | |
del Blanco et al. | Visual tracking of multiple interacting objects through Rao-Blackwellized data association particle filtering |