241 research outputs found
Recommended from our members
Deep Learning for Action Understanding in Video
Action understanding is key to automatically analyzing video content and thus is important for many real-world applications such as autonomous driving car, robot-assisted care, etc. Therefore, in the computer vision field, action understanding has been one of the fundamental research topics. Most conventional methods for action understanding are based on hand-crafted features. Like the recent advances seen in image classification, object detection, image captioning, etc, deep learning has become a popular approach for action understanding in video. However, there remain several important research challenges in developing deep learning based methods for understanding actions. This thesis focuses on the development of effective deep learning methods for solving three major challenges.
Action detection at fine granularities in time: Previous work in deep learning based action understanding mainly focuses on exploring various backbone networks that are designed for the video-level action classification task. These did not explore the fine-grained temporal characteristics and thus failed to produce temporally precise estimation of action boundaries. In order to understand actions more comprehensively, it is important to detect actions at finer granularities in time. In Part I, we study both segment-level action detection and frame-level action detection. Segment-level action detection is usually formulated as the temporal action localization task, which requires not only recognizing action categories for the whole video but also localizing the start time and end time of each action instance. To this end, we propose an effective multi-stage framework called Segment-CNN consisting of three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes the learned classification network to localize each action instance. In another approach, frame-level action detection is effectively formulated as the per-frame action labeling task. We combine two reverse operations (i.e. convolution and deconvolution) into a joint Convolutional-De-Convolutional (CDC) filter, which simultaneously conducts downsampling in space and upsampling in time to jointly model both high-level semantics and temporal dynamics. We design a novel CDC network to predict actions at frame-level and the frame-level predictions can be further used to detect precise segment boundary for the temporal action localization task. Our method not only improves the state-of-the-art mean Average Precision (mAP) result on THUMOS’14 from 41.3% to 44.4% for the per-frame labeling task, but also improves mAP for the temporal action localization task from 19.0% to 23.3% on THUMOS’14 and from 16.4% to 23.8% on ActivityNet v1.3.
Action detection in the constrained scenarios: The usual training process of deep learning models consists of supervision and data, which are not always available in reality. In Part II, we consider the scenarios of incomplete supervision and incomplete data. For incomplete supervision, we focus on the weakly-supervised temporal action localization task and propose AutoLoc which is the first framework that can directly predict the temporal boundary of each action instance with only the video-level annotations available during training. To enable the training of such a boundary prediction model, we design a novel Outer-Inner-Contrastive (OIC) loss to help discover the segment-level supervision and we prove that the OIC loss is differentiable to the underlying boundary prediction model. Our method significantly improves mAP on THUMOS14 from 13.7% to 21.2% and mAP on ActivityNet from 7.4% to 27.3%. For the scenario of incomplete data, we formulate a novel task called Online Detection of Action Start (ODAS) in streaming videos to enable detecting the action start time on the fly when a live video action is just starting. ODAS is important in many applications such as early alert generation to allow timely security or emergency response. Specifically, we propose three novel methods to address the challenges in training ODAS models: (1) hard negative samples generation based on Generative Adversarial Network (GAN) to distinguish ambiguous background, (2) explicitly modeling the temporal consistency between data around action start and data succeeding action start, and (3) adaptive sampling strategy to handle the scarcity of training data.
Action understanding in the compressed domain: The mainstream action understanding methods including the aforementioned techniques developed by us require first decoding the compressed video into RGB image frames. This may result in significant cost in terms of storage and computation. Recently, researchers started to investigate how to directly perform action understanding in the compressed domain in order to achieve high efficiency while maintaining the state-of-the-art action detection accuracy. The key research challenge is developing effective backbone networks that can directly take data in the compressed domain as input. Our baseline is to take models developed for action understanding in the decoded domain and adapt them to attack the same tasks in the compressed domain. In Part III, we address two important issues in developing the backbone networks that exclusively operate in the compressed domain. First, compressed videos may be produced by different encoders or encoding parameters, but it is impractical to train a different compressed-domain action understanding model for each different format. We experimentally analyze the effect of video encoder variation and develop a simple yet effective training data preparation method to alleviate the sensitivity to encoder variation. Second, motion cues have been shown to be important for action understanding, but the motion vectors in compressed video are often very noisy and not discriminative enough for directly performing accurate action understanding. We develop a novel and highly efficient framework called DMC-Net that can learn to predict discriminative motion cues based on noisy motion vectors and residual errors in the compressed video streams. On three action recognition benchmarks, namely HMDB-51, UCF101 and a subset of Kinetics, we demonstrate that our DMC-Net can significantly shorten the performance gap between state-of-the-art compressed video based methods with and without optical flow, while being two orders of magnitude faster than the methods that use optical flow.
By addressing the three major challenges mentioned above, we are able to develop more robust models for video action understanding and improve performance in various dimensions, such as (1) temporal precision, (2) required levels of supervision, (3) live video analysis ability, and finally (4) efficiency in processing compressed video. Our research has contributed significantly to advancing the state of the art of video action understanding and expanding the foundation for comprehensive semantic understanding of video content
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Model-Based Environmental Visual Perception for Humanoid Robots
The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling
Recommended from our members
Contour and texture for visual recognition of object categories
The recognition of categories of objects in images has become a central
topic in computer vision. Automatic visual recognition systems
are rapidly becoming central to applications such as image search,
robotics, vehicle safety systems, and image editing. This work addresses
three sub-problems of recognition: image classification, object
detection, and semantic segmentation. The task of classification
is to determine whether an object of a particular category is present
or not. Object detection aims to localize any objects of the category.
Semantic segmentation is a more complete image understanding,
whereby an image is partitioned into coherent regions that are assigned
meaningful class labels. This thesis proposes novel discriminative
learning approaches to these problems.
Our primary contributions are threefold. Firstly, we demonstrate
that the contours (the outline and interior edges) of an object are,
alone, sufficient for accurate visual recognition. Secondly, we propose
two powerful new feature types: (i) a learned codebook of contour
fragments matched with an improved oriented chamfer distance,
and (ii) a set of texture-based features that simultaneously exploit
local appearance, approximate shape, and appearance context.
The efficacy of these new features types is evaluated on a wide variety
of datasets. Thirdly, we show how, in combination, these two
largely orthogonal feature types can substantially improve recognition
performance above that achieved by either alone
Context-driven Object Detection and Segmentation with Auxiliary Information
One fundamental problem in computer vision and robotics is to
localize objects of interest in an image. The task can either be
formulated as an object detection problem if the objects are
described by a set of pose parameters, or an object segmentation
one if we recover object boundary precisely. A key issue in
object detection and segmentation concerns exploiting the spatial
context, as local evidence is often insufficient to determine
object pose in the presence of heavy occlusions or large object
appearance variations. This thesis addresses the object detection
and segmentation problem in such adverse conditions with
auxiliary depth data provided by RGBD cameras. We focus on four
main issues in context-aware object detection and segmentation:
1) what are the effective context representations? 2) how can we
work with limited and imperfect depth data? 3) how to design
depth-aware features and integrate depth cues into conventional
visual inference tasks? 4) how to make use of unlabeled data to
relax the labeling requirements for training data?
We discuss three object detection and segmentation scenarios
based on varying amounts of available auxiliary information. In
the first case, depth data are available for model training but
not available for testing. We propose a structured Hough voting
method for detecting objects with heavy occlusion in indoor
environments, in which we extend the Hough hypothesis space to
include both the object's location, and its visibility pattern.
We design a new score function that accumulates votes for object
detection and occlusion prediction. In addition, we explore the
correlation between objects and their environment, building a
depth-encoded object-context model based on RGBD data. In the
second case, we address the problem of localizing glass objects
with noisy and incomplete depth data. Our method integrates the
intensity and depth information from a single view point, and
builds a Markov Random Field that predicts glass boundary and
region jointly. In addition, we propose a nonparametric,
data-driven label transfer scheme for local glass boundary
estimation. A weighted voting scheme based on a joint feature
manifold is adopted to integrate depth and appearance cues, and
we learn a distance metric on the depth-encoded feature manifold.
In the third case, we make use of unlabeled data to relax the
annotation requirements for object detection and segmentation,
and propose a novel data-dependent margin distribution learning
criterion for boosting, which utilizes the intrinsic geometric
structure of datasets. One key aspect of this method is that it
can seamlessly incorporate unlabeled data by including a graph
Laplacian regularizer. We demonstrate the performance of our
models and compare with baseline methods on several real-world
object detection and segmentation tasks, including indoor object
detection, glass object segmentation and foreground segmentation
in video
Scene Mapping and Understanding by Robotic Vision
The first mechanical Automaton concept was found in a Chinese
text written in the 3rd century BC, while Computer Vision was born
in the late 1960s. Therefore, visual perception applied to machines (i.e. the Machine Vision) is a young and exciting alliance.
When robots came in, the new field of Robotic Vision was born, and these terms began to be erroneously interchanged. In short, we can say that Machine Vision is an engineering domain, which concern the industrial use of Vision. The Robotic Vision, instead, is a research field that tries to incorporate robotics aspects in computer vision algorithms. Visual Servoing, for example, is one of the problems that cannot be solved by computer vision only. Accordingly, a large part of this work deals with boosting popular Computer Vision techniques by exploiting robotics: e.g. the use of kinematics to localize a vision sensor, mounted as the robot end-effector. The remainder of this work is dedicated to the counterparty, i.e. the use of computer vision to solve real robotic problems like grasping objects or navigate avoiding obstacles. Will be presented a brief survey about mapping data structures most widely used in robotics along with SkiMap, a novel sparse data structure created both for robotic mapping and as a general purpose 3D spatial index. Thus, several approaches to implement Object Detection and Manipulation, by exploiting the aforementioned mapping strategies, will be proposed, along with a completely new Machine Teaching facility in order to simply the training procedure of modern Deep Learning networks
IMAGE CLASSIFICATION USING INVARIANT LOCAL FEATURES AND CONTEXTUAL INFORMATION
Ph.DDOCTOR OF PHILOSOPH
Object detection for big data
"May 2014."Dissertation supervisor: Dr. Tony X. Han.Includes vita.We have observed significant advances in object detection over the past few decades and gladly seen the related research has began to contribute to the world: Vehicles could automatically stop before hitting any pedestrian; Face detectors have been integrated into smart phones and tablets; Video surveillance systems could locate the suspects and stop crimes. All these applications demonstrate the substantial research progress on object detection. However learning a robust object detector is still quite challenging due to the fact that object detection is a very unbalanced big data problem. In this dissertation, we aim at improving the object detector's performance from different aspects. For object detection, the state-of-the-art performance is achieved through supervised learning. The performances of object detectors of this kind are mainly determined by two factors: features and underlying classification algorithms. We have done thorough research on both of these factors. Our contribution involves model adaption, local learning, contextual boosting, template learning and feature development. Since the object detection is an unbalanced problem, in which positive examples are hard to be collected, we propose to adapt a general object detector for a specific scenario with a few positive examples; To handle the large intra-class variation problem lying in object detection task, we propose a local adaptation method to learn a set of efficient and effective detectors for a single object category; To extract the effective context from the huge amount of negative data in object detection, we introduce a novel contextual descriptor to iteratively improve the detector; To detect object with a depth sensor, we design an effective depth descriptor; To distinguish the object categories with the similar appearance, we propose a local feature embedding and template selection algorithm, which has been successfully incorporated into a real-world fine-grained object recognition application. All the proposed algorithms and featuIncludes bibliographical references (pages 117-130)
- …