AutoGraph: Predicting Lane Graphs from Traffic Observations

Lane graph estimation is a long-standing problem in the context of autonomous driving. Previous works aimed at solving this problem by relying on large-scale, hand-annotated lane graphs, introducing a data bottleneck for training models to solve this task. To overcome this limitation, we propose to use the motion patterns of traffic participants as lane graph annotations. In our AutoGraph approach, we employ a pre-trained object tracker to collect the tracklets of traffic participants such as vehicles and trucks. Based on the location of these tracklets, we predict the successor lane graph from an initial position using overhead RGB images only, not requiring any human supervision. In a subsequent stage, we show how the individual successor predictions can be aggregated into a consistent lane graph. We demonstrate the efficacy of our approach on the UrbanLaneGraph dataset and perform extensive quantitative and qualitative evaluations, indicating that AutoGraph is on par with models trained on hand-annotated graph data. Model and dataset will be made available at redacted-for-review.Comment: 8 pages, 6 figure

Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely \emph{deep attributes} exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201

Occlusion reasoning for multiple object visual tracking

Thesis (Ph.D.)--Boston UniversityOcclusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications. In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions. Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "nonmaximum suppression" of hypotheses by modeling the likelihood of the entire image. The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches

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

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.Comment: doctoral dissertatio

Training Algorithms for Multiple Object Tracking

Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking pedestrians, and other types of objects, that often exhibit predictable behaviours, that allow us, as humans, to track those objects. Nevertheless, most existing approaches rely solely on simple affinity or appearance cues to maintain the identities of the tracked objects, ignoring their behaviour. This presents a challenge when objects of interest are invisible or indistinguishable for a long period of time. In this thesis, we focus on enhancing the quality of multiple object trackers by learning and exploiting the long ranging models of object behaviour. Such behaviours come in different forms, be it a physical model of the ball motion, model of interaction between the ball and the players in sports or motion patterns of pedestrians or cars, that is specific to a particular scene. In the first part of the thesis, we begin with the task of tracking the ball and the players in team sports. We propose a model that tracks both types of objects simultaneously, while respecting the physical laws of ball motion when in free fall, and interaction constraints that appear when players are in the possession of the ball. We show that both the presence of the behaviour models and the simultaneous solution of both tasks aids the performance of tracking, in basketball, volleyball, and soccer. In the second part of the thesis, we focus on motion models of pedestrian and car behaviour that emerge in the outdoor scenes. Such motion models are inherently global, as they determine where people starting from one location tend to end up much later in time. Imposing such global constraints while keeping the tracking problem tractable presents a challenge, which is why many approaches rely on local affinity measures. We formulate a problem of simultaneously tracking the objects and learning their behaviour patterns. We show that our approach, when applied in conjunction with a number of state-of-the-art trackers, improves their performance, by forcing their output to follow the learned motion patterns of the scene. In the last part of the thesis, we study a new emerging class of models for multiple object tracking, that appeared recently due to availability of large scale datasets - sequence models for multiple object tracking. While such models could potentially learn arbitrarily long ranging behaviours, training them presents several challenges. We propose a training scheme and a loss function that allows to significantly improve the quality of training of such models. We demonstrate that simply using our training scheme and loss allows to learn scoring function for trajectories, which enables us to outperform state-of-the-art methods on several tracking benchmarks

People detection and tracking in crowded scenes

People are often a central element of visual scenes, particularly in real-world street scenes. Thus it has been a long-standing goal in Computer Vision to develop methods aiming at analyzing humans in visual data. Due to the complexity of real-world scenes, visual understanding of people remains challenging for machine perception. In this thesis we focus on advancing the techniques for people detection and tracking in crowded street scenes. We also propose new models for human pose estimation and motion segmentation in realistic images and videos. First, we propose detection models that are jointly trained to detect single person as well as pairs of people under varying degrees of occlusion. The learning algorithm of our joint detector facilitates a tight integration of tracking and detection, because it is designed to address common failure cases during tracking due to long-term inter-object occlusions. Second, we propose novel multi person tracking models that formulate tracking as a graph partitioning problem. Our models jointly cluster detection hypotheses in space and time, eliminating the need for a heuristic non-maximum suppression. Furthermore, for crowded scenes, our tracking model encodes long-range person re-identification information into the detection clustering process in a unified and rigorous manner. Third, we explore the visual tracking task in different granularity. We present a tracking model that simultaneously clusters object bounding boxes and pixel level trajectories over time. This approach provides a rich understanding of the motion of objects in the scene. Last, we extend our tracking model for the multi person pose estimation task. We introduce a joint subset partitioning and labelling model where we simultaneously estimate the poses of all the people in the scene. In summary, this thesis addresses a number of diverse tasks that aim to enable vision systems to analyze people in realistic images and videos. In particular, the thesis proposes several novel ideas and rigorous mathematical formulations, pushes the boundary of state-of-the-arts and results in superior performance.Personen sind oft ein zentraler Bestandteil visueller Szenen, besonders in natürlichen Straßenszenen. Daher ist es seit langem ein Ziel der Computer Vision, Methoden zu entwickeln, um Personen in einer Szene zu analysieren. Aufgrund der Komplexität natürlicher Szenen bleibt das visuelle Verständnis von Personen eine Herausforderung für die maschinelle Wahrnehmung. Im Zentrum dieser Arbeit steht die Weiterentwicklung von Verfahren zur Detektion und zum Tracking von Personen in Straßenszenen mit Menschenmengen. Wir erforschen darüber hinaus neue Methoden zur menschlichen Posenschätzung und Bewegungssegmentierung in realistischen Bildern und Videos. Zunächst schlagen wir Detektionsmodelle vor, die gemeinsam trainiert werden, um sowohl einzelne Personen als auch Personenpaare bei verschiedener Verdeckung zu detektieren. Der Lernalgorithmus unseres gemeinsamen Detektors erleichtert eine enge Integration von Tracking und Detektion, da er darauf konzipiert ist, häufige Fehlerfälle aufgrund langfristiger Verdeckungen zwischen Objekten während des Tracking anzugehen. Zweitens schlagen wir neue Modelle für das Tracking mehrerer Personen vor, die das Tracking als Problem der Graphenpartitionierung formulieren. Unsere Mod- elle clustern Detektionshypothesen gemeinsam in Raum und Zeit und eliminieren dadurch die Notwendigkeit einer heuristischen Unterdrückung nicht maximaler De- tektionen. Bei Szenen mit Menschenmengen kodiert unser Trackingmodell darüber hinaus einheitlich und genau Informationen zur langfristigen Re-Identifizierung in den Clusteringprozess der Detektionen. Drittens untersuchen wir die visuelle Trackingaufgabe bei verschiedener Gran- ularität. Wir stellen ein Trackingmodell vor, das im Zeitablauf gleichzeitig Begren- zungsrahmen von Objekten und Trajektorien auf Pixelebene clustert. Diese Herange- hensweise ermöglicht ein umfassendes Verständnis der Bewegung der Objekte in der Szene. Schließlich erweitern wir unser Trackingmodell für die Posenschätzung mehrerer Personen. Wir führen ein Modell zur gemeinsamen Graphzerlegung und Knoten- klassifikation ein, mit dem wir gleichzeitig die Posen aller Personen in der Szene schätzen. Zusammengefasst widmet sich diese Arbeit einer Reihe verschiedener Aufgaben mit dem gemeinsamen Ziel, Bildverarbeitungssystemen die Analyse von Personen in realistischen Bildern und Videos zu ermöglichen. Insbesondere schlägt die Arbeit mehrere neue Ansätze und genaue mathematische Formulierungen vor, und sie zeigt Methoden, welche die Grenze des neuesten Stands der Technik überschreiten und eine höhere Leistung von Bildverarbeitungssystemen ermöglichen

Multiple Object Tracking in Urban Traffic Scenes

RÉSUMÉ:Le suivi multiobjets (MOT) est un domaine très étudié qui a évolué et changé beaucoup durant les années grâce à ses plusieurs applications potentielles pour améliorer notre qualité de vie. Dans notre projet de recherche, spécifiquement, nous sommes intéressés par le MOT dans les scènes de trafic urbain pour extraire précisément les trajectoires des usagers de la route, afin d’améliorer les systèmes de circulation routière desquels nous bénéficions tous.Notre première contribution est l’introduction d’informations sur les étiquettes de classe dans l’ensemble des caractéristiques qui décrivent les objets pour les associer sur différents trames, afin de bien capturer leur mouvement sous forme de trajectoires dans un environnement réel.Nous capitalisons sur les informations provenant d’un détecteur basé sur l’apprentissage profond qui est utilisé pour l’extraction des objets d’intérêt avant la procédure de suivi, carnous avons été intrigués par leurs popularités croissantes et les bonnes performances qu’ils obtiennent. Cependant, malgré leur potentiel prometteur dans la littérature, nous avons constaté que les résultats étaient décevants dans nos expériences. La qualité des détections,telle que postulée, affecte grandement la qualité des trajectoires finales. Néanmoins, nous avons observé que les informations des étiquettes de classe, ainsi que son score de confiance, sont très utiles pour notre application, où il y a un nombre élevé de variabilité pour les types d’usagers de la route.Ensuite, nous avons concentré nos efforts sur la fusion des entrées de deux sources différentes afin d’obtenir un ensemble d’objets en entrée avec un niveau de précision satisfaisant pour procéder à l’étape de suivi. À ce stade, nous avons travaillé sur l’intégration des boîtes englobantes à partir d’un détecteur multi-classes par apprentissage et d’une méthode basée sur la soustraction d’arrière-plan pour résoudre les problèmes tels que la fragmentation et les représentations redondantes du même objet.---------- ABSTRACT:Multiple object tracking (MOT) is an intensively researched area that have evolved and undergone much innovation throughout the years due to its potential in a lot of applications to improve our quality of life. In our research project, specifically, we are interested in applying MOT in urban traffic scenes to portray an accurate representation of the road user trajectories for the eventual improvements of road traffic systems that affect people from all walks of life. Our first contribution is the introduction of class label information as part of the features that describe the targets and for associating them across frames to capture their motion into trajectories in real environment. We capitalize on that information from a deep learning detector that is used for extraction of objects of interest prior to the tracking procedure, since we were intrigued by their growing popularity and reported good performances. However,despite their promising potential in the literature, we found that the results were disappointing in our experiments. The quality of extracted input, as postulated, critically affects the quality of the final trajectories obtained as tracking output. Nevertheless, we observed that the class label information, along with its confidence score, is invaluable for our application of urban traffic settings where there are a high number of variability in terms of types of road users. Next, we focused our effort on fusing inputs from two different sources in order to obtain a set of objects with a satisfactory level of accuracy to proceed with the tracking stage. At this point, we worked on the integration of the bounding boxes from a learned multi-class object detector and a background subtraction-based method to resolve issues, such as fragmentation and redundant representations of the same object