Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain

Detecting camouflaged moving foreground objects has been known to be difficult due to the similarity between the foreground objects and the background. Conventional methods cannot distinguish the foreground from background due to the small differences between them and thus suffer from under-detection of the camouflaged foreground objects. In this paper, we present a fusion framework to address this problem in the wavelet domain. We first show that the small differences in the image domain can be highlighted in certain wavelet bands. Then the likelihood of each wavelet coefficient being foreground is estimated by formulating foreground and background models for each wavelet band. The proposed framework effectively aggregates the likelihoods from different wavelet bands based on the characteristics of the wavelet transform. Experimental results demonstrated that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects. Specifically, the average F-measure for the proposed algorithm was 0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

Active and Physics-Based Human Pose Reconstruction

Perceiving humans is an important and complex problem within computervision. Its significance is derived from its numerous applications, suchas human-robot interaction, virtual reality, markerless motion capture,and human tracking for autonomous driving. The difficulty lies in thevariability in human appearance, physique, and plausible body poses. Inreal-world scenes, this is further exacerbated by difficult lightingconditions, partial occlusions, and the depth ambiguity stemming fromthe loss of information during the 3d to 2d projection. Despite thesechallenges, significant progress has been made in recent years,primarily due to the expressive power of deep neural networks trained onlarge datasets. However, creating large-scale datasets with 3dannotations is expensive, and capturing the vast diversity of the realworld is demanding. Traditionally, 3d ground truth is captured usingmotion capture laboratories that require large investments. Furthermore,many laboratories cannot easily accommodate athletic and dynamicmotions. This thesis studies three approaches to improving visualperception, with emphasis on human pose estimation, that can complementimprovements to the underlying predictor or training data.The first two papers present active human pose estimation, where areinforcement learning agent is tasked with selecting informativeviewpoints to reconstruct subjects efficiently. The papers discard thecommon assumption that the input is given and instead allow the agent tomove to observe subjects from desirable viewpoints, e.g., those whichavoid occlusions and for which the underlying pose estimator has a lowprediction error.The third paper introduces the task of embodied visual active learning,which goes further and assumes that the perceptual model is notpre-trained. Instead, the agent is tasked with exploring its environmentand requesting annotations to refine its visual model. Learning toexplore novel scenarios and efficiently request annotation for new datais a step towards life-long learning, where models can evolve beyondwhat they learned during the initial training phase. We study theproblem for segmentation, though the idea is applicable to otherperception tasks.Lastly, the final two papers propose improving human pose estimation byintegrating physical constraints. These regularize the reconstructedmotions to be physically plausible and serve as a complement to currentkinematic approaches. Whether a motion has been observed in the trainingdata or not, the predictions should obey the laws of physics. Throughintegration with a physical simulator, we demonstrate that we can reducereconstruction artifacts and enforce, e.g., contact constraints

Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown