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Mitesh Patel

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6 papers

2 author rows

EAAI Journal 2024 Journal Article

F2M: Ensemble-based uncertainty estimation model for fire detection in indoor environments

Matej Arlović
Mitesh Patel
Josip Balen
Franko Hržić

Early fire detection and timely notification are paramount for preventing human and material casualties caused by fire. As a result, scientists have developed various fire monitoring systems based on sensors and images. Image-based systems have proven more advantageous than sensor-based ones as they provide additional details about the fire, such as its location, intensity, and progression. However, accurately determining the shape and boundaries of flames in image-based systems is difficult due to various factors, including backgrounds, different fire sizes, and interference from objects that resemble flames. In this study, we first evaluate convolutional neural networks used in related research and benchmark them on our challenging indoor fire dataset. Secondly, we propose a novel Feature Merging Model (F2M), which combines the output fire segmentation masks of the top five segmentation models obtained through the performed evaluation. The F2M relies on a novel mechanism that introduces bias into the mask estimation and achieves better results than all the tested methods, along with Monte-Carlo dropout for uncertainty estimation. The F2M ensemble-based uncertainty estimation model achieved improvements in comparison to the best performance convolutional neural network U-Net++, on three different image resolutions: 256 × 256, 640 × 640 and 800 × 800, according to the following metrics: Total error, Dice coefficient, and IoU score.

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ICRA Conference 2018 Conference Paper

ContextualNet: Exploiting Contextual Information Using LSTMs to Improve Image-Based Localization

Mitesh Patel
Brendan Emery
Yan-Ying Chen

Convolutional Neural Networks (CNN) have successfully been utilized for localization using a single monocular image [1]. Most of the work to date has either focused on reducing the dimensionality of data for better learning of parameters during training or on developing different variations of CNN models to improve pose estimation. Many of the best performing works solely consider the content in a single image, while the context from historical images is ignored. In this paper, we propose a combined CNN-LSTM which is capable of incorporating contextual information from historical images to better estimate the current pose. Experimental results achieved using a dataset collected in an indoor office space improved the overall system results to 0. 8 m & 2. 5° at the third quartile of the cumulative distribution as compared with 1. 5 m & 3. 0° achieved by PoseNet [1]. Furthermore, we demonstrate how the temporal information exploited by the CNN-LSTM model assists in localizing the robot in situations where image content does not have sufficient features.

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ICRA Conference 2018 Conference Paper

Optimizing Placement and Number of RF Beacons to Achieve Better Indoor Localization

Raphael Falque
Mitesh Patel
Jacob T. Biehl

In this paper, we propose a novel solution to optimize the deployment of Radio Frequency (RF) beacons for the purpose of indoor localization. We propose a system that optimizes both the number of beacons and their placement in a given environment. We propose a novel cost-function, called CovBsm, that allows to simultaneously optimize the 3-coverage while maximizing the beacon spreading. Using this cost function, we propose a framework that maximize both the number of beacons and their placement in a given environment. The proposed solution accounts for the indoor infrastructure and its influence on the RF signal propagation by embedding a realistic simulator into the optimization process.

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ICRA Conference 2017 Conference Paper

Gaussian processes online observation classification for RSSI-based low-cost indoor positioning systems

Maani Ghaffari
Mitesh Patel
Jaime Valls Miró

In this paper, we propose a real-time classification scheme to cope with noisy Radio Signal Strength Indicator (RSSI) measurements utilized in indoor positioning systems. RSSI values are often converted to distances for position estimation. However due to multipathing and shadowing effects, finding a unique sensor model using both parametric and non-parametric methods is highly challenging. We learn decision regions using the Gaussian Processes classification to accept measurements that are consistent with the operating sensor model. The proposed approach can perform online, does not rely on a particular sensor model or parameters, and is robust to sensor failures. The experimental results achieved using hardware show that available positioning algorithms can benefit from incorporating the classifier into their measurement model as a meta-sensor modeling technique.

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ICRA Conference 2014 Conference Paper

A probabilistic approach to learn activities of daily living of a mobility aid device user

Mitesh Patel
Jaime Valls Miró
Gamini Dissanayake

The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid.

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ICRA Conference 2013 Conference Paper

Language for learning complex human-object interactions

Mitesh Patel
Carl Henrik Ek
Nikolaos Kyriazis
Antonis A. Argyros
Jaime Valls Miró
Danica Kragic

In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.

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