Event-Based Motion Segmentation by Motion Compensation

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video: https://youtu.be/0q6ap_OSBA

Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability

Video segmentation encompasses a wide range of categories of problem formulation, e.g., object, scene, actor-action and multimodal video segmentation, for delineating task-specific scene components with pixel-level masks. Recently, approaches in this research area shifted from concentrating on ConvNet-based to transformer-based models. In addition, various interpretability approaches have appeared for transformer models and video temporal dynamics, motivated by the growing interest in basic scientific understanding, model diagnostics and societal implications of real-world deployment. Previous surveys mainly focused on ConvNet models on a subset of video segmentation tasks or transformers for classification tasks. Moreover, component-wise discussion of transformer-based video segmentation models has not yet received due focus. In addition, previous reviews of interpretability methods focused on transformers for classification, while analysis of video temporal dynamics modelling capabilities of video models received less attention. In this survey, we address the above with a thorough discussion of various categories of video segmentation, a component-wise discussion of the state-of-the-art transformer-based models, and a review of related interpretability methods. We first present an introduction to the different video segmentation task categories, their objectives, specific challenges and benchmark datasets. Next, we provide a component-wise review of recent transformer-based models and document the state of the art on different video segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc interpretability methods for transformer models and interpretability methods for understanding the role of the temporal dimension in video models. Finally, we conclude our discussion with future research directions

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Soft computing and non-parametric techniques for effective video surveillance systems

Esta tesis propone varios objetivos interconectados para el diseño de un sistema de vídeovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una métrica de evaluación del detector y sistema de seguimiento basada en una mínima referencia. Dicha técnica es una respuesta a la demanda de ajuste de forma rápida y fácil del sistema adecuándose a distintos entornos. También se propone una técnica de optimización basada en Estrategias Evolutivas y la combinación de funciones de idoneidad en varios pasos. El objetivo es obtener los parámetros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construcción de un clasificador basado en técnicas no paramétricas que pudieran modelar la distribución de datos de entrada independientemente de la fuente de generación de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patrón de tiempo que puede ser fácilmente modelado mediante HMMs. La propuesta consiste en una modificación del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emisión del HMM mediante una técnica no paramétrica basada en estimación de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring system whose operation is thought for a wide rank of conditions. Firstly an evaluation technique of the detector and tracking system is proposed and it is based on a minimum reference or ground-truth. This technique is an answer to the demand of fast and easy adjustment of the system adapting itself to different contexts. Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and the combination of fitness functions. The objective is to obtain the parameters of adjustment of the detector and tracking system for the best operation in an ample range of possible situations. Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique models the distribution of data regardless the source generation of such data. Short term detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric technique based on the density estimation with kernels (KDE)

BEYOND MULTI-TARGET TRACKING: STATISTICAL PATTERN ANALYSIS OF PEOPLE AND GROUPS

Ogni giorno milioni e milioni di videocamere monitorano la vita quotidiana delle persone, registrando e collezionando una grande quantit\ue0 di dati. Questi dati possono essere molto utili per scopi di video-sorveglianza: dalla rilevazione di comportamenti anomali all'analisi del traffico urbano nelle strade. Tuttavia i dati collezionati vengono usati raramente, in quanto non \ue8 pensabile che un operatore umano riesca a esaminare manualmente e prestare attenzione a una tale quantit\ue0 di dati simultaneamente. Per questo motivo, negli ultimi anni si \ue8 verificato un incremento della richiesta di strumenti per l'analisi automatica di dati acquisiti da sistemi di video-sorveglianza in modo da estrarre informazione di pi\uf9 alto livello (per esempio, John, Sam e Anne stanno camminando in gruppo al parco giochi vicino alla stazione) a partire dai dati a disposizione che sono solitamente a basso livello e ridondati (per esempio, una sequenza di immagini). L'obiettivo principale di questa tesi \ue8 quello di proporre soluzioni e algoritmi automatici che permettono di estrarre informazione ad alto livello da una zona di interesse che viene monitorata da telecamere. Cos\uec i dati sono rappresentati in modo da essere facilmente interpretabili e analizzabili da qualsiasi persona. In particolare, questo lavoro \ue8 focalizzato sull'analisi di persone e i loro comportamenti sociali collettivi. Il titolo della tesi, beyond multi-target tracking, evidenzia lo scopo del lavoro: tutti i metodi proposti in questa tesi che si andranno ad analizzare hanno come comune denominatore il target tracking. Inoltre andremo oltre le tecniche standard per arrivare a una rappresentazione del dato a pi\uf9 alto livello. Per prima cosa, analizzeremo il problema del target tracking in quanto \ue8 alle basi di questo lavoro. In pratica, target tracking significa stimare la posizione di ogni oggetto di interesse in un immagine e la sua traiettoria nel tempo. Analizzeremo il problema da due prospettive complementari: 1) il punto di vista ingegneristico, dove l'obiettivo \ue8 quello di creare algoritmi che ottengono i risultati migliori per il problema in esame. 2) Il punto di vista della neuroscienza: motivati dalle teorie che cercano di spiegare il funzionamento del sistema percettivo umano, proporremo in modello attenzionale per tracking e il riconoscimento di oggetti e persone. Il secondo problema che andremo a esplorare sar\ue0 l'estensione del tracking alla situazione dove pi\uf9 telecamere sono disponibili. L'obiettivo \ue8 quello di mantenere un identificatore univoco per ogni persona nell'intera rete di telecamere. In altre parole, si vuole riconoscere gli individui che vengono monitorati in posizioni e telecamere diverse considerando un database di candidati. Tale problema \ue8 chiamato in letteratura re-indetificazione di persone. In questa tesi, proporremo un modello standard di come affrontare il problema. In questo modello, presenteremo dei nuovi descrittori di aspetto degli individui, in quanto giocano un ruolo importante allo scopo di ottenere i risultati migliori. Infine raggiungeremo il livello pi\uf9 alto di rappresentazione dei dati che viene affrontato in questa tesi, che \ue8 l'analisi di interazioni sociali tra persone. In particolare, ci focalizzeremo in un tipo specifico di interazione: il raggruppamento di persone. Proporremo dei metodi di visione computazionale che sfruttano nozioni di psicologia sociale per rilevare gruppi di persone. Inoltre, analizzeremo due modelli probabilistici che affrontano il problema di tracking (congiunto) di gruppi e individui.Every day millions and millions of surveillance cameras monitor the world, recording and collecting huge amount of data. The collected data can be extremely useful: from the behavior analysis to prevent unpleasant events, to the analysis of the traffic. However, these valuable data is seldom used, because of the amount of information that the human operator has to manually attend and examine. It would be like looking for a needle in the haystack. The automatic analysis of data is becoming mandatory for extracting summarized high-level information (e.g., John, Sam and Anne are walking together in group at the playground near the station) from the available redundant low-level data (e.g., an image sequence). The main goal of this thesis is to propose solutions and automatic algorithms that perform high-level analysis of a camera-monitored environment. In this way, the data are summarized in a high-level representation for a better understanding. In particular, this work is focused on the analysis of moving people and their collective behaviors. The title of the thesis, beyond multi-target tracking, mirrors the purpose of the work: we will propose methods that have the target tracking as common denominator, and go beyond the standard techniques in order to provide a high-level description of the data. First, we investigate the target tracking problem as it is the basis of all the next work. Target tracking estimates the position of each target in the image and its trajectory over time. We analyze the problem from two complementary perspectives: 1) the engineering point of view, where we deal with problem in order to obtain the best results in terms of accuracy and performance. 2) The neuroscience point of view, where we propose an attentional model for tracking and recognition of objects and people, motivated by theories of the human perceptual system. Second, target tracking is extended to the camera network case, where the goal is to keep a unique identifier for each person in the whole network, i.e., to perform person re-identification. The goal is to recognize individuals in diverse locations over different non-overlapping camera views or also the same camera, considering a large set of candidates. In this context, we propose a pipeline and appearance-based descriptors that enable us to define in a proper way the problem and to reach the-state-of-the-art results. Finally, the higher level of description investigated in this thesis is the analysis (discovery and tracking) of social interaction between people. In particular, we focus on finding small groups of people. We introduce methods that embed notions of social psychology into computer vision algorithms. Then, we extend the detection of social interaction over time, proposing novel probabilistic models that deal with (joint) individual-group tracking

Learning by correlation for computer vision applications: from Kernel methods to deep learning

Learning to spot analogies and differences within/across visual categories is an arguably powerful approach in machine learning and pattern recognition which is directly inspired by human cognition. In this thesis, we investigate a variety of approaches which are primarily driven by correlation and tackle several computer vision applications

Learning Video Representation from Self-supervision

This thesis investigates the problem of learning video representations for video understanding. Previous works have explored the use of data-driven deep learning approaches, which have been shown to be effective in learning useful video representations. However, obtaining large amounts of labeled data can be costly and time-consuming. We investigate self-supervised approach as for multimodal video data to overcome this challenge. Video data typically contains multiple modalities, such as visual, audio, transcribed speech, and textual captions, which can serve as pseudo-labels for representation learning without needing manual labeling. By utilizing these modalities, we can train deep representations over large-scale video data consisting of millions of video clips collected from the internet. We demonstrate the scalability benefits of multimodal self-supervision by achieving new state-of-the-art performance in various domains, including video action recognition, text-to-video retrieval, and text-to-video grounding. We also examine the limitations of these approaches, which often rely on the association assumption involving multiple modalities of data used in self-supervision. For example, the text transcript is often assumed to be about the video content, and two segments of the same video share similar semantics. To overcome this problem, we propose new methods for learning video representations with more intelligent sampling strategies to capture samples that share high-level semantics or consistent concepts. The proposed methods include a clustering component to address false negative pairs in multimodal paired contrastive learning, a novel sampling strategy for finding visually groundable video-text pairs, an investigation of object tracking supervision for temporal association, and a new multimodal task for demonstrating the effectiveness of the proposed model. We aim to develop more robust and generalizable video representations for real-world applications, such as human-to-robot interaction and event extraction from large-scale news sources