Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

S3Aug: Segmentation, Sampling, and Shift for Action Recognition

Action recognition is a well-established area of research in computer vision. In this paper, we propose S3Aug, a video data augmenatation for action recognition. Unlike conventional video data augmentation methods that involve cutting and pasting regions from two videos, the proposed method generates new videos from a single training video through segmentation and label-to-image transformation. Furthermore, the proposed method modifies certain categories of label images by sampling to generate a variety of videos, and shifts intermediate features to enhance the temporal coherency between frames of the generate videos. Experimental results on the UCF101, HMDB51, and Mimetics datasets demonstrate the effectiveness of the proposed method, paricularlly for out-of-context videos of the Mimetics dataset

A Review of Dynamic Datasets for Facial Expression Research

Temporal dynamics have been increasingly recognized as an important component of facial expressions. With the need for appropriate stimuli in research and application, a range of databases of dynamic facial stimuli has been developed. The present article reviews the existing corpora and describes the key dimensions and properties of the available sets. This includes a discussion of conceptual features in terms of thematic issues in dataset construction as well as practical features which are of applied interest to stimulus usage. To identify the most influential sets, we further examine their citation rates and usage frequencies in existing studies. General limitations and implications for emotion research are noted and future directions for stimulus generation are outlined

Research and Practice on Fusion of Visual and Audio Perception

随着监控系统智能化的快速发展，监控数据在交通、环境、安防等领域发挥着越来越重要的作用。受人类感知模型的启发，利用音频数据与视频数据的互补效应对场景进行感知具有较好地研究价值。然而随之产生的海量监控数据越来越难以检索，这迫使人们寻找更加有效地分析方法，从而将人从重复的劳动中解脱出来。因此，音视频融合感知技术不仅具有重要的理论研究价值，在应用前景上也是大有可为。 本文研究了当前音视频融合感知领域发展的现状，以传统视频监控平台为基础，设计了音视频融合感知的体系结构。立足于音视频内容分析，研究了基于音视频融合感知的暴力场景分析模型。本文主要贡献如下： 1. 以音视频融合感知的监控平台为出发点，设计...With the rapid development of intelligent monitoring system, monitoring data is playing an increasingly important role in traffic, environment, security and the other fields. Inspired by the model of human perception, people use the complementary effect of audio and visual data to percept the scene. And then the huge amount of visual-audio data forces people to look for a more effective way to ana...学位：工学硕士院系专业：信息科学与技术学院_计算机科学与技术学号：2302012115292

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm

From pixels to people : recovering location, shape and pose of humans in images

Humans are at the centre of a significant amount of research in computer vision. Endowing machines with the ability to perceive people from visual data is an immense scientific challenge with a high degree of direct practical relevance. Success in automatic perception can be measured at different levels of abstraction, and this will depend on which intelligent behaviour we are trying to replicate: the ability to localise persons in an image or in the environment, understanding how persons are moving at the skeleton and at the surface level, interpreting their interactions with the environment including with other people, and perhaps even anticipating future actions. In this thesis we tackle different sub-problems of the broad research area referred to as "looking at people", aiming to perceive humans in images at different levels of granularity. We start with bounding box-level pedestrian detection: We present a retrospective analysis of methods published in the decade preceding our work, identifying various strands of research that have advanced the state of the art. With quantitative exper- iments, we demonstrate the critical role of developing better feature representations and having the right training distribution. We then contribute two methods based on the insights derived from our analysis: one that combines the strongest aspects of past detectors and another that focuses purely on learning representations. The latter method outperforms more complicated approaches, especially those based on hand- crafted features. We conclude our work on pedestrian detection with a forward-looking analysis that maps out potential avenues for future research. We then turn to pixel-level methods: Perceiving humans requires us to both separate them precisely from the background and identify their surroundings. To this end, we introduce Cityscapes, a large-scale dataset for street scene understanding. This has since established itself as a go-to benchmark for segmentation and detection. We additionally develop methods that relax the requirement for expensive pixel-level annotations, focusing on the task of boundary detection, i.e. identifying the outlines of relevant objects and surfaces. Next, we make the jump from pixels to 3D surfaces, from localising and labelling to fine-grained spatial understanding. We contribute a method for recovering 3D human shape and pose, which marries the advantages of learning-based and model- based approaches. We conclude the thesis with a detailed discussion of benchmarking practices in computer vision. Among other things, we argue that the design of future datasets should be driven by the general goal of combinatorial robustness besides task-specific considerations.Der Mensch steht im Zentrum vieler Forschungsanstrengungen im Bereich des maschinellen Sehens. Es ist eine immense wissenschaftliche Herausforderung mit hohem unmittelbarem Praxisbezug, Maschinen mit der Fähigkeit auszustatten, Menschen auf der Grundlage von visuellen Daten wahrzunehmen. Die automatische Wahrnehmung kann auf verschiedenen Abstraktionsebenen erfolgen. Dies hängt davon ab, welches intelligente Verhalten wir nachbilden wollen: die Fähigkeit, Personen auf der Bildfläche oder im 3D-Raum zu lokalisieren, die Bewegungen von Körperteilen und Körperoberflächen zu erfassen, Interaktionen einer Person mit ihrer Umgebung einschließlich mit anderen Menschen zu deuten, und vielleicht sogar zukünftige Handlungen zu antizipieren. In dieser Arbeit beschäftigen wir uns mit verschiedenen Teilproblemen die dem breiten Forschungsgebiet "Betrachten von Menschen" gehören. Beginnend mit der Fußgängererkennung präsentieren wir eine Analyse von Methoden, die im Jahrzehnt vor unserem Ausgangspunkt veröffentlicht wurden, und identifizieren dabei verschiedene Forschungsstränge, die den Stand der Technik vorangetrieben haben. Unsere quantitativen Experimente zeigen die entscheidende Rolle sowohl der Entwicklung besserer Bildmerkmale als auch der Trainingsdatenverteilung. Anschließend tragen wir zwei Methoden bei, die auf den Erkenntnissen unserer Analyse basieren: eine Methode, die die stärksten Aspekte vergangener Detektoren kombiniert, eine andere, die sich im Wesentlichen auf das Lernen von Bildmerkmalen konzentriert. Letztere übertrifft kompliziertere Methoden, insbesondere solche, die auf handgefertigten Bildmerkmalen basieren. Wir schließen unsere Arbeit zur Fußgängererkennung mit einer vorausschauenden Analyse ab, die mögliche Wege für die zukünftige Forschung aufzeigt. Anschließend wenden wir uns Methoden zu, die Entscheidungen auf Pixelebene betreffen. Um Menschen wahrzunehmen, müssen wir diese sowohl praezise vom Hintergrund trennen als auch ihre Umgebung verstehen. Zu diesem Zweck führen wir Cityscapes ein, einen umfangreichen Datensatz zum Verständnis von Straßenszenen. Dieser hat sich seitdem als Standardbenchmark für Segmentierung und Erkennung etabliert. Darüber hinaus entwickeln wir Methoden, die die Notwendigkeit teurer Annotationen auf Pixelebene reduzieren. Wir konzentrieren uns hierbei auf die Aufgabe der Umgrenzungserkennung, d. h. das Erkennen der Umrisse relevanter Objekte und Oberflächen. Als nächstes machen wir den Sprung von Pixeln zu 3D-Oberflächen, vom Lokalisieren und Beschriften zum präzisen räumlichen Verständnis. Wir tragen eine Methode zur Schätzung der 3D-Körperoberfläche sowie der 3D-Körperpose bei, die die Vorteile von lernbasierten und modellbasierten Ansätzen vereint. Wir schließen die Arbeit mit einer ausführlichen Diskussion von Evaluationspraktiken im maschinellen Sehen ab. Unter anderem argumentieren wir, dass der Entwurf zukünftiger Datensätze neben aufgabenspezifischen Überlegungen vom allgemeinen Ziel der kombinatorischen Robustheit bestimmt werden sollte

Fine-grained action recognition by motion saliency and mid-level patches

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition