675 research outputs found
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology
With the advancement of deep neural networks and computer vision-based Human
Activity Recognition, employment of Point-Cloud Data technologies (LiDAR,
mmWave) has seen a lot interests due to its privacy preserving nature. Given
the high promise of accurate PCD technologies, we develop, PALMAR, a
multiple-inhabitant activity recognition system by employing efficient signal
processing and novel machine learning techniques to track individual person
towards developing an adaptive multi-inhabitant tracking and HAR system. More
specifically, we propose (i) a voxelized feature representation-based real-time
PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive
Order Hidden Markov Model based multi-person tracking and crossover ambiguity
reduction techniques and (iii) novel adaptive deep learning-based domain
adaptation technique to improve the accuracy of HAR in presence of data
scarcity and diversity (device, location and population diversity). We
experimentally evaluate our framework and systems using (i) a real-time PCD
collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants,
(ii) one publicly available 3D LiDAR activity data (28 participants) and (iii)
an embedded hardware prototype system which provided promising HAR performances
in multi-inhabitants (96%) scenario with a 63% improvement of multi-person
tracking than state-of-art framework without losing significant system
performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications
202
Tracking interacting targets in multi-modal sensors
PhDObject tracking is one of the fundamental tasks in various applications such as surveillance,
sports, video conferencing and activity recognition. Factors such as occlusions,
illumination changes and limited field of observance of the sensor make tracking a challenging
task. To overcome these challenges the focus of this thesis is on using multiple
modalities such as audio and video for multi-target, multi-modal tracking. Particularly,
this thesis presents contributions to four related research topics, namely, pre-processing of
input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking,
and interaction recognition.
To improve the performance of detection algorithms, especially in the presence
of noise, this thesis investigate filtering of the input data through spatio-temporal feature
analysis as well as through frequency band analysis. The pre-processed data from multiple
modalities is then fused within Particle filtering (PF). To further minimise the discrepancy
between the real and the estimated positions, we propose a strategy that associates the
hypotheses and the measurements with a real target, using a Weighted Probabilistic Data
Association (WPDA). Since the filtering involved in the detection process reduces the
available information and is inapplicable on low signal-to-noise ratio data, we investigate
simultaneous detection and tracking approaches and propose a multi-target track-beforedetect
Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses
the detection step and performs tracking in the raw signal. Finally, we apply the proposed
multi-modal tracking to recognise interactions between targets in regions within, as well
as outside the cameras’ fields of view.
The efficiency of the proposed approaches are demonstrated on large uni-modal,
multi-modal and multi-sensor scenarios from real world detections, tracking and event
recognition datasets and through participation in evaluation campaigns
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
Minimal Infrastructure Radio Frequency Home Localisation Systems
The ability to track the location of a subject in their home allows the provision of a
number of location based services, such as remote activity monitoring, context sensitive
prompts and detection of safety critical situations such as falls. Such pervasive monitoring
functionality offers the potential for elders to live at home for longer periods of their lives
with minimal human supervision.
The focus of this thesis is on the investigation and development of a home roomlevel
localisation technique which can be readily deployed in a realistic home environment
with minimal hardware requirements. A conveniently deployed Bluetooth ®
localisation
platform is designed and experimentally validated throughout the thesis. The platform
adopts the convenience of a mobile phone and the processing power of a remote location
calculation computer. The use of Bluetooth ®
also ensures the extensibility of the platform
to other home health supervision scenarios such as wireless body sensor monitoring.
Central contributions of this work include the comparison of probabilistic and nonprobabilistic
classifiers for location prediction accuracy and the extension of probabilistic
classifiers to a Hidden Markov Model Bayesian filtering framework. New location
prediction performance metrics are developed and signicant performance improvements
are demonstrated with the novel extension of Hidden Markov Models to higher-order
Markov movement models. With the simple probabilistic classifiers, location is correctly
predicted 80% of the time. This increases to 86% with the application of the Hidden
Markov Models and 88% when high-order Hidden Markov Models are employed.
Further novelty is exhibited in the derivation of a real-time Hidden Markov Model
Viterbi decoding algorithm which presents all the advantages of the original algorithm,
while producing location estimates in real-time. Significant contributions are also made
to the field of human gait-recognition by applying Bayesian filtering to the task of motion
detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even
enables a
floor recognition rate of 68% using only accelerometers. The unique application
of time-varying Hidden Markov Models demonstrates the effect of integrating these freely
available motion predictions on long-term location predictions
Probabilistic modeling of texture transition for fast tracking and delineation
In this thesis a probabilistic approach to texture boundary detection for tracking applications is presented. We have developed a novel fast algorithm for Bayesian estimation of texture transition locations from a short sequence of pixels on a scanline that combines the desirable speed of edge-based line search and the sophistication of Bayesian texture analysis given a small set of observations. For the cases where the given observations are too few for reliable Bayesian estimation of probability of texture change we propose an innovative machine learning technique to generate a probabilistic texture transition model. This is achieved by considering a training dataset containing small patches of blending textures. By encompassing in the training set enough examples to accurately model texture transitions of interest we can construct a predictor that can be used for object boundary tracking that can deal with few observations and demanding cases of tracking of arbitrary textured objects against cluttered background. Object outlines are then obtained by combining the texture crossing probabilities across a set of scanlines. We show that a rigid geometric model of the object to be tracked or smoothness constraints in the absence of such a model can be used to coalesce the scanline texture crossing probabilities obtained using the methods mentioned above. We propose a Hidden Markov Model to aggregate robustly the sparse transition probabilities of scanlines sampled along the projected hypothesis model contour. As a result continuous object contours can be extracted using a posteriori maximization of texture transition probabilities. On the other hand, stronger geometric constraints such as available rigid models of the target are directly enforced by robust stochastic optimization. In addition to being fast, the allure of the proposed probabilistic framework is that it accommodates a unique infrastructure for tracking of heterogeneous objects which utilizes the machine learning-based predictor as well as the Bayesian estimator interchangeably in conjunction with robust optimization to extract object contours robustly. We apply the developed methods to tracking of textured and non textured rigid objects as well as deformable body outlines and monocular articulated human motion in challenging conditions. Finally, because it is fast, our method can also serve as an interactive texture segmentation tool
Minimum s-Excess Graph for Segmenting and Tracking Multiple Borders with HMM
We present a novel HMM based approach to simultaneous segmentation of vessel walls in Lymphatic confocal images. The vessel borders are parameterized using RBFs to minimize the number of tracking points. The proposed method tracks the hidden states that indicate border locations for both the inner and outer walls. The observation for both borders is obtained using edge-based features from steerable filters. Two separate Gaussian probability distributions for the vessel borders and background are used to infer the emission probability, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into a minimization of an s-excess graph cost, with each node in the graph corresponding to a hidden state and the weight for each node being defined by its emission probability. We define the inter-relations between neighboring nodes based on the transmission probability. We present both qualitative and quantitative analysis in comparison to the popular Viterbi algorithm
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