3D-Hog Embedding Frameworks for Single and Multi-Viewpoints Action Recognition Based on Human Silhouettes

This paper has been presented at : 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-Histogram of Oriented Gradient (3D-HOG) embedding. We exploit fast-computational approaches involving Principal Component Analysis (PCA) over the local feature spaces for compactly describing actions as combinations of local gestures and L 2 -Regularized Logistic Regression (L 2 -RLR) for learning the action model from local features. Outperforming results on Weizmann and i3DPost datasets confirm efficacy of the proposed approaches as compared to the baseline method and other works, in terms of accuracy and robustness to appearance changes

Novel methods for posture-based human action recognition and activity anomaly detection

PhD ThesisArti cial Intelligence (AI) for Human Action Recognition (HAR) and Human Activity Anomaly Detection (HAAD) is an active and exciting research eld. Video-based HAR aims to classify human actions and video-based HAAD aims to detect abnormal human activities within data. However, a human is an extremely complex subject and a non-rigid object in the video, which provides great challenges for Computer Vision and Signal Processing. Relevant applications elds are surveillance and public monitoring, assisted living, robotics, human-to-robot interaction, prosthetics, gaming, video captioning, and sports analysis. The focus of this thesis is on the posture-related HAR and HAAD. The aim is to design computationally-e cient, machine and deep learning-based HAR and HAAD methods which can run in multiple humans monitoring scenarios. This thesis rstly contributes two novel 3D Histogram of Oriented Gradient (3D-HOG) driven frameworks for silhouette-based HAR. The 3D-HOG state-of-the-art limitations, e.g. unweighted local body areas based processing and unstable performance over di erent training rounds, are addressed. The proposed methods achieve more accurate results than the baseline, outperforming the state-of-the-art. Experiments are conducted on publicly available datasets, alongside newly recorded data. This thesis also contributes a new algorithm for human poses-based HAR. In particular, the proposed human poses-based HAR is among the rst, few, simultaneous attempts which have been conducted at the time. The proposed HAR algorithm, named ActionXPose, is based on Convolutional Neural Networks and Long Short-Term Memory. It turns out to be more reliable and computationally advantageous when compared to human silhouette-based approaches. The ActionXPose's exibility also allows crossdatasets processing and more robustness to occlusions scenarios. Extensive evaluation on publicly available datasets demonstrates the e cacy of ActionXPose over the state-of-the-art. Moreover, newly recorded data, i.e. Intelligent Sensing Lab Dataset (ISLD), is also contributed and exploited to further test ActionXPose in real-world, non-cooperative scenarios. The last set of contributions in this thesis regards pose-driven, combined HAR and HAAD algorithms. Motivated by ActionXPose achievements, this thesis contributes a new algorithm to simultaneously extract deep-learningbased features from human-poses, RGB Region of Interests (ROIs) and detected objects positions. The proposed method outperforms the stateof- the-art in both HAR and HAAD. The HAR performance is extensively tested on publicly available datasets, including the contributed ISLD dataset. Moreover, to compensate for the lack of data in the eld, this thesis also contributes three new datasets for human-posture and objects-positions related HAAD, i.e. BMbD, M-BMdD and JBMOPbD datasets

Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática

Temporal salience based human action recognition

This paper proposes a new approach for human action recognition exploring the temporal salience. We exploit features over the temporal saliency maps for learning the action representation using a local dense descriptor. This approach automatically guides the descriptor towards the most interesting contents, i.e. the salience region, and obtains the action representation using solely the saliency information. Outperforming results on Weizmann, DHA and KTH datasets confirm the efficiency of the proposed approach as compared to the state-of-the-art methods, in terms of accuracy and robustness to the variations inside the action and similarities among actions. The proposed method outperforms by 2.7% with DHA, 1% with KTH and it is comparable in the case of Weizmann

Modeling temporal visual salience for human action recognition enabled visual anonymity preservation

This paper proposes a novel approach for visually anonymizing video clips while retaining the ability to machine-based analysis of the video clip, such as, human action recognition. The visual anonymization is achieved by proposing a novel method for generating the anonymization silhouette by modeling the frame-wise temporal visual salience. This is followed by analysing these temporal salience-based silhouettes by extracting the proposed histograms of gradients in salience ( HOG-S ) for learning the action representation in the visually anonymized domain. Since the anonymization maps are based on the temporal salience maps represented in gray scale, only the moving body parts related to the motion of the action are represented in larger gray values forming highly anonymized silhouettes, resulting in the highest mean anonymity score (MAS), the least identifiable visual appearance attributes and a high utility of human-perceived utility in action recognition. In terms of machine-based human action recognition, using the proposed HOG-S features has resulted in the highest accuracy rate in the anonymized domain compared to those achieved from the existing anonymization methods. Overall, the proposed holistic human action recognition method, i.e. , the temporal salience modeling followed by the HOG-S feature extraction, has resulted in the best human action recognition accuracy rates for datasets DHA, KTH, UIUC1, UCF Sports and HMDB51 with improvements of 3%, 1.6%, 0.8%, 1.3% and 16.7%, respectively. The proposed method outperforms both feature-based and deep learning based existing approaches

REPRESENTATION LEARNING FOR ACTION RECOGNITION

The objective of this research work is to develop discriminative representations for human actions. The motivation stems from the fact that there are many issues encountered while capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration), inter-action similarity, background motion, and occlusion of actors. Hence, obtaining a representation which can address all the variations in the same action while maintaining discrimination with other actions is a challenging task. In literature, actions have been represented either using either low-level or high-level features. Low-level features describe the motion and appearance in small spatio-temporal volumes extracted from a video. Due to the limited space-time volume used for extracting low-level features, they are not able to account for viewpoint and actor variations or variable length actions. On the other hand, high-level features handle variations in actors, viewpoints, and duration but the resulting representation is often high-dimensional which introduces the curse of dimensionality. In this thesis, we propose new representations for describing actions by combining the advantages of both low-level and high-level features. Specifically, we investigate various linear and non-linear decomposition techniques to extract meaningful attributes in both high-level and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build action-specific dictionaries. Each dictionary retains only the discriminative information for a particular action and hence reduces inter-action similarity. Then, a sparsity-based classification method is proposed to classify the low-rank representation of clips obtained using these dictionaries. We show that this representation based on dictionary learning improves the classification performance across actions. Also, a few of the actions consist of rapid body deformations that hinder the extraction of local features from body movements. Hence, we propose to use a dictionary which is trained on convolutional neural network (CNN) features of the human body in various poses to reliably identify actors from the background. Particularly, we demonstrate the efficacy of sparse representation in the identification of the human body under rapid and substantial deformation. In the first two approaches, sparsity-based representation is developed to improve discriminability using class-specific dictionaries that utilize action labels. However, developing an unsupervised representation of actions is more beneficial as it can be used to both recognize similar actions and localize actions. We propose to exploit inter-action similarity to train a universal attribute model (UAM) in order to learn action attributes (common and distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation, a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains redundant attributes of all other actions, we use factor analysis to extract a novel lowvi dimensional action-vector representation for each clip. Action-vectors are shown to suppress background motion and highlight actions of interest in both trimmed and untrimmed clips that contributes to action recognition without the help of any classifiers. It is observed during our experiments that action-vector cannot effectively discriminate between actions which are visually similar to each other. Hence, we subject action-vectors to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary information across action-vectors using different local features followed by discriminative embedding provides the best classification performance. Further, we explore non-linear embedding of action-vectors using Siamese networks especially for fine-grained action recognition. A visualization of the hidden layer output in Siamese networks shows its ability to effectively separate visually similar actions. This leads to better classification performance than linear embedding on fine-grained action recognition. All of the above approaches are presented on large unconstrained datasets with hundreds of examples per action. However, actions in surveillance videos like snatch thefts are difficult to model because of the diverse variety of scenarios in which they occur and very few labeled examples. Hence, we propose to utilize the universal attribute model (UAM) trained on large action datasets to represent such actions. Specifically, we show that there are similarities between certain actions in the large datasets with snatch thefts which help in extracting a representation for snatch thefts using the attributes from the UAM. This representation is shown to be effective in distinguishing snatch thefts from regular actions with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing actions which provide better discrimination than existing representations. The first approach presents a dictionary learning based sparse representation for effective discrimination of actions. Also, we propose a sparse representation for the human body based on dictionaries in order to recognize actions with rapid body deformations. In the next approach, a low-dimensional representation called action-vector for unsupervised action recognition is presented. Further, linear and non-linear embedding of action-vectors is proposed for addressing inter-action similarity and fine-grained action recognition, respectively. Finally, we propose a representation for locating snatch thefts among thousands of regular interactions in surveillance videos

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growing demand driven by the applications, predicting 3D pose from a 2D image is a challenging and ill-posed problem due to the loss of depth information during projection from 3D to 2D. Although there have been years of research on 3D pose estimation problem, it still remains unsolved. In this thesis, we propose a variety of ways to tackle the 3D pose estimation problem both for articulated human bodies and rigid object bodies by learning robust features and latent representations. First, we present a novel video-based approach that exploits spatiotemporal features for 3D human pose estimation in a discriminative regression scheme. While early approaches typically account for motion information by temporally regularizing noisy pose estimates in individual frames, we demonstrate that taking into account motion information very early in the modeling process with spatiotemporal features yields significant performance improvements. We further propose a CNN-based motion compensation approach that stabilizes and centralizes the human body in the bounding boxes of consecutive frames to increase the reliability of spatiotemporal features. This then allows us to effectively overcome ambiguities and improve pose estimation accuracy. Second, we develop a novel Deep Learning framework for structured prediction of 3D human pose. Our approach relies on an auto-encoder to learn a high-dimensional latent pose representation that accounts for joint dependencies. We combine traditional CNNs for supervised learning with auto-encoders for structured learning and demonstrate that our approach outperforms the existing ones both in terms of structure preservation and prediction accuracy. Third, we propose a 3D human pose estimation approach that relies on a two-stream neural network architecture to simultaneously exploit 2D joint location heatmaps and image features. We show that 2D pose of a person, predicted in terms of heatmaps by a fully convolutional network, provides valuable cues to disambiguate challenging poses and results in increased pose estimation accuracy. We further introduce a novel and generic trainable fusion scheme, which automatically learns where and how to fuse the features extracted from two different input modalities that a two-stream neural network operates on. Our trainable fusion framework selects the optimal network architecture on-the-fly and improves upon standard hard-coded network architectures. Fourth, we propose an efficient approach to estimate 3D pose of objects from a single RGB image. Existing methods typically detect 2D bounding boxes and then predict the object pose using a pipelined approach. The redundancy in different parts of the architecture makes such methods computationally expensive. Moreover, the final pose estimation accuracy depends on the accuracy of the intermediate 2D object detection step. In our method, the object is classified and its pose is regressed in a single shot from the full image using a single, compact fully convolutional neural network. Our approach achieves the state-of-the-art accuracy without requiring any costly pose refinement step and runs in real-time at 50 fps on a modern GPU, which is at least 5X faster than the state of the art

Physical Adversarial Attacks for Surveillance: A Survey

Modern automated surveillance techniques are heavily reliant on deep learning methods. Despite the superior performance, these learning systems are inherently vulnerable to adversarial attacks - maliciously crafted inputs that are designed to mislead, or trick, models into making incorrect predictions. An adversary can physically change their appearance by wearing adversarial t-shirts, glasses, or hats or by specific behavior, to potentially avoid various forms of detection, tracking and recognition of surveillance systems; and obtain unauthorized access to secure properties and assets. This poses a severe threat to the security and safety of modern surveillance systems. This paper reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications. In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework. Furthermore, we review and analyze strategies to defend against the physical adversarial attacks and the methods for evaluating the strengths of the defense. The insights in this paper present an important step in building resilience within surveillance systems to physical adversarial attacks

Unsupervised object candidate discovery for activity recognition

Die automatische Interpretation menschlicher Bewegungsabläufe auf Basis von Videos ist ein wichtiger Bestandteil vieler Anwendungen im Bereich des Maschinellen Sehens, wie zum Beispiel Mensch-Roboter Interaktion, Videoüberwachung, und inhaltsbasierte Analyse von Multimedia Daten. Anders als die meisten Ansätze auf diesem Gebiet, die hauptsächlich auf die Klassifikation von einfachen Aktionen, wie Aufstehen, oder Gehen ausgerichtet sind, liegt der Schwerpunkt dieser Arbeit auf der Erkennung menschlicher Aktivitäten, d.h. komplexer Aktionssequenzen, die meist Interaktionen des Menschen mit Objekten beinhalten. Gemäß der Aktionsidentifikationstheorie leiten menschliche Aktivitäten ihre Bedeutung nicht nur von den involvierten Bewegungsmustern ab, sondern vor allem vom generellen Kontext, in dem sie stattfinden. Zu diesen kontextuellen Informationen gehören unter anderem die Gesamtheit aller vorher furchgeführter Aktionen, der Ort an dem sich die aktive Person befindet, sowie die Menge der Objekte, die von ihr manipuliert werden. Es ist zum Beispiel nicht möglich auf alleiniger Basis von Bewegungsmustern und ohne jeglicher Miteinbeziehung von Objektwissen zu entschieden ob eine Person, die ihre Hand zum Mund führt gerade etwas isst oder trinkt, raucht, oder bloß die Lippen abwischt. Die meisten Arbeiten auf dem Gebiet der computergestützten Aktons- und Aktivitätserkennung ignorieren allerdings jegliche durch den Kontext bedingte Informationen und beschränken sich auf die Identifikation menschlicher Aktivitäten auf Basis der beobachteten Bewegung. Wird jedoch Objektwissen für die Klassifikation miteinbezogen, so geschieht dies meist unter Zuhilfenahme von überwachten Detektoren, für deren Einrichtung widerum eine erhebliche Menge an Trainingsdaten erforderlich ist. Bedingt durch die hohen zeitlichen Kosten, die die Annotation dieser Trainingsdaten mit sich bringt, wird das Erweitern solcher Systeme, zum Beispiel durch das Hinzufügen neuer Typen von Aktionen, zum eigentlichen Flaschenhals. Ein weiterer Nachteil des Hinzuziehens von überwacht trainierten Objektdetektoren, ist deren Fehleranfälligkeit, selbst wenn die verwendeten Algorithmen dem neuesten Stand der Technik entsprechen. Basierend auf dieser Beobachtung ist das Ziel dieser Arbeit die Leistungsfähigkeit computergestützter Aktivitätserkennung zu verbessern mit Hilfe der Hinzunahme von Objektwissen, welches im Gegensatz zu den bisherigen Ansätzen ohne überwachten Trainings gewonnen werden kann. Wir Menschen haben die bemerkenswerte Fähigkeit selektiv die Aufmerksamkeit auf bestimmte Regionen im Blickfeld zu fokussieren und gleichzeitig nicht relevante Regionen auszublenden. Dieser kognitive Prozess erlaubt es uns unsere beschränkten Bewusstseinsressourcen unbewusst auf Inhalte zu richten, die anschließend durch das Gehirn ausgewertet werden. Zum Beispiel zur Interpretation visueller Muster als Objekte eines bestimmten Typs. Die Regionen im Blickfeld, die unsere Aufmerksamkeit unbewusst anziehen werden als Proto-Objekte bezeichnet. Sie sind definiert als unbestimmte Teile des visuellen Informationsspektrums, die zu einem späteren Zeitpunkt durch den Menschen als tatsächliche Objekte wahrgenommen werden können, wenn er seine Aufmerksamkeit auf diese richtet. Einfacher ausgedrückt: Proto-Objekte sind Kandidaten für Objekte, oder deren Bestandteile, die zwar lokalisiert aber noch nicht identifiziert wurden. Angeregt durch die menschliche Fähigkeit solche visuell hervorstechenden (salienten) Regionen zuverlässig vom Hintergrund zu unterscheiden, haben viele Wissenschaftler Methoden entwickelt, die es erlauben Proto-Objekte zu lokalisieren. Allen diesen Algorithmen ist gemein, dass möglichst wenig statistisches Wissens über tatsächliche Objekte vorausgesetzt wird. Visuelle Aufmerksamkeit und Objekterkennung sind sehr eng miteinander vernküpfte Prozesse im visuellen System des Menschen. Aus diesem Grund herrscht auf dem Gebiet des Maschinellen Sehens ein reges Interesse an der Integration beider Konzepte zur Erhöhung der Leistung aktueller Bilderkennungssysteme. Die im Rahmen dieser Arbeit entwickelten Methoden gehen in eine ähnliche Richtung: wir demonstrieren, dass die Lokalisation von Proto-Objekten es erlaubt Objektkandidaten zu finden, die geeignet sind als zusätzliche Modalität zu dienen für die bewegungsbasierte Erkennung menschlicher Aktivitäten. Die Grundlage dieser Arbeit bildet dabei ein sehr effizienter Algorithmus, der die visuelle Salienz mit Hilfe von quaternionenbasierten DCT Bildsignaturen approximiert. Zur Extraktion einer Menge geeigneter Objektkandidaten (d.h. Proto-Objekten) aus den resultierenden Salienzkarten, haben wir eine Methode entwickelt, die den kognitiven Mechanismus des Inhibition of Return implementiert. Die auf diese Weise gewonnenen Objektkandidaten nutzen wir anschliessend in Kombination mit state-of-the-art Bag-of-Words Methoden zur Merkmalsbeschreibung von Bewegungsmustern um komplexe Aktivitäten des täglichen Lebens zu klassifizieren. Wir evaluieren das im Rahmen dieser Arbeit entwickelte System auf diversen häufig genutzten Benchmark-Datensätzen und zeigen experimentell, dass das Miteinbeziehen von Proto-Objekten für die Aktivitätserkennung zu einer erheblichen Leistungssteigerung führt im Vergleich zu rein bewegungsbasierten Ansätzen. Zudem demonstrieren wir, dass das vorgestellte System bei der Erkennung menschlicher Aktivitäten deutlich weniger Fehler macht als eine Vielzahl von Methoden, die dem aktuellen Stand der Technik entsprechen. Überraschenderweise übertrifft unser System leistungsmäßig sogar Verfahren, die auf Objektwissen aufbauen, welches von überwacht trainierten Detektoren, oder manuell erstellten Annotationen stammt. Benchmark-Datensätze sind ein sehr wichtiges Mittel zum quantitativen Vergleich von computergestützten Mustererkennungsverfahren. Nach einer Überprüfung aller öffentlich verfügbaren, relevanten Benchmarks, haben wir jedoch festgestellt, dass keiner davon geeignet war für eine detaillierte Evaluation von Methoden zur Erkennung komplexer, menschlicher Aktivitäten. Aus diesem Grund bestand ein Teil dieser Arbeit aus der Konzeption und Aufnahme eines solchen Datensatzes, des KIT Robo-kitchen Benchmarks. Wie der Name vermuten lässt haben wir uns dabei für ein Küchenszenario entschieden, da es ermöglicht einen großen Umfang an Aktivitäten des täglichen Lebens einzufangen, von denen viele Objektmanipulationen enthalten. Um eine möglichst umfangreiche Menge natürlicher Bewegungen zu erhalten, wurden die Teilnehmer während der Aufnahmen kaum eingeschränkt in der Art und Weise wie die diversen Aktivitäten auszuführen sind. Zu diesem Zweck haben wir den Probanden nur die Art der auszuführenden Aktivität mitgeteilt, sowie wo die benötigten Gegenstände zu finden sind, und ob die jeweilige Tätigkeit am Küchentisch oder auf der Arbeitsplatte auszuführen ist. Dies hebt KIT Robo-kitchen deutlich hervor gegenüber den meisten existierenden Datensätzen, die sehr unrealistisch gespielte Aktivitäten enthalten, welche unter Laborbedingungen aufgenommen wurden. Seit seiner Veröffentlichung wurde der resultierende Benchmark mehrfach verwendet zur Evaluation von Algorithmen, die darauf abzielen lang andauerne, realistische, komplexe, und quasi-periodische menschliche Aktivitäten zu erkennen