Unsupervised understanding of location and illumination changes in egocentric videos

Betancourt, Alejandro; Díaz-Rodríguez, Natalia; Barakova, Emilia; Rauterberg, Matthias; Regazzoni, Carlo S.

doi:10.1016/j.pmcj.2017.03.016

Cited by 5 publications

(5 citation statements)

References 43 publications

(72 reference statements)

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“…The most intuitive method to understand the data complexity is by visualizing its features and the respective classes. This approach is generally restricted to low dimensional data (2D or 3D) or simplified versions of the feature space that are obtained using manifold algorithms such as Principal Components Analysis (PCA), IsoMaps, or Self Organizing Maps [46]. A common approach that is used when working with dimensional data is to determine its complexity by comparing the capabilities of different classification algorithms to capture known patterns.…”

Section: Classification Modelmentioning

confidence: 99%

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

2017

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Slum identification in urban settlements is a crucial step in the process of formulation of pro-poor policies. However, the use of conventional methods for slum detection such as field surveys can be time-consuming and costly. This paper explores the possibility of implementing a low-cost standardized method for slum detection. We use spectral, texture and structural features extracted from very high spatial resolution imagery as input data and evaluate the capability of three machine learning algorithms (Logistic Regression, Support Vector Machine and Random Forest) to classify urban areas as slum or no-slum. Using data from Buenos Aires (Argentina), Medellin (Colombia) and Recife (Brazil), we found that Support Vector Machine with radial basis kernel delivers the best performance (with F2-scores over 0.81). We also found that singularities within cities preclude the use of a unified classification model.

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Section: Classification Modelmentioning

confidence: 99%

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

2017

Self Cite

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“…Understanding of locations, in terms of mapping the surrounding area with image features or semantically labeling the environment, is actively under research in egocentric vision. In [22], a combination of scene illumination and distinct location characteristics is learned in an unsupervised way, in order to enhance the usability of wearable cameras for hand detection. Location recognition is indirectly the task in [23] where a Google Glass application captures images of the user's field of view and retrieves information about the buildings in sight.…”

Section: Objects and Location Classificationmentioning

confidence: 99%

Object Detection-Based Location and Activity Classification from Egocentric Videos: A Systematic Analysis

Kapidis

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Dam

et al. 2019

Computer Communications and Networks

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Egocentric vision has emerged in the daily practice of application domains such as lifelogging, activity monitoring, robot navigation and the analysis of social interactions. Plenty of research focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that indoor locations and daily activities can be characterized by the presence of specific objects. Objects can be obtained either from laborious human annotations or automatically, using vision-based detectors. We perform a study regarding the use of object detections as input for location and activity classification and analyze the influence of various detection parameters. We compare our detections against manually provided object labels and show that location classification is affected by detection quality and quantity. Utilization of the temporal structure in object detections mitigates the consequences of noisy ones. Moreover, we determine that the recognition of activities is related to the presence of specific objects and that the lack of explicit associations between certain activities and objects hurts classification performance for these activities. Finally, we discuss the outcomes of each task and our method's potential for real-world applications.

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“…Understanding of locations, in terms of mapping the surrounding area or labeling the environment, has been under active research in egocentric vision. In [14], an unsupervised way of combining scene illumination and location characteristics is proposed to enhance the usability of wearable cameras. Location recognition is indirectly the task in [15] where a Google Glass application captures images of the user's field of view and retrieves information about the buildings in sight.…”

Section: Kitchenmentioning

confidence: 99%

Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos

Kapidis

Poppe

Dam

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Egocentric vision is a technology that exists in a variety of fields such as life-logging, sports recording and robot navigation. Plenty of research work focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that locations can be characterized by the presence of specific objects. Our objective is the recognition of locations in egocentric videos that mainly consist of indoor house scenes. We perform an extensive comparison between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based classification methods that aim at finding the in-house location by classifying the detected objects which are extracted with a state-of-the-art object detector. We show that location classification is affected by the quality of the detected objects, i.e. the false detections among the correct ones in a series of frames, but this effect can be greatly limited by taking into account the temporal structure of the information by using LSTM. Finally, we argue about the potential for useful real-world applications.

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Unsupervised understanding of location and illumination changes in egocentric videos

Cited by 5 publications

References 43 publications

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

Object Detection-Based Location and Activity Classification from Egocentric Videos: A Systematic Analysis

Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos

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