2,584 research outputs found
Embodied Visual Perception Models For Human Behavior Understanding
Many modern applications require extracting the core attributes of human behavior such as a person\u27s attention, intent, or skill level from the visual data. There are two main challenges related to this problem. First, we need models that can represent visual data in terms of object-level cues. Second, we need models that can infer the core behavioral attributes from the visual data. We refer to these two challenges as ``learning to see\u27\u27, and ``seeing to learn\u27\u27 respectively. In this PhD thesis, we have made progress towards addressing both challenges.
We tackle the problem of ``learning to see\u27\u27 by developing methods that extract object-level information directly from raw visual data. This includes, two top-down contour detectors, DeepEdge and HfL, which can be used to aid high-level vision tasks such as object detection. Furthermore, we also present two semantic object segmentation methods, Boundary Neural Fields (BNFs), and Convolutional Random Walk Networks (RWNs), which integrate low-level affinity cues into an object segmentation process. We then shift our focus to video-level understanding, and present a Spatiotemporal Sampling Network (STSN), which can be used for video object detection, and discriminative motion feature learning.
Afterwards, we transition into the second subproblem of ``seeing to learn\u27\u27, for which we leverage first-person GoPro cameras that record what people see during a particular activity. We aim to infer the core behavior attributes such as a person\u27s attention, intention, and his skill level from such first-person data. To do so, we first propose a concept of action-objects--the objects that capture person\u27s conscious visual (watching a TV) or tactile (taking a cup) interactions. We then introduce two models, EgoNet and Visual-Spatial Network (VSN), which detect action-objects in supervised and unsupervised settings respectively. Afterwards, we focus on a behavior understanding task in a complex basketball activity. We present a method for evaluating players\u27 skill level from their first-person basketball videos, and also a model that predicts a player\u27s future motion trajectory from a single first-person image
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page
https://research.google.com/ava/ for detail
Evaluating Two-Stream CNN for Video Classification
Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets for video analytics. Among the many practical needs,
classifying videos (or video clips) based on their major semantic categories
(e.g., "skiing") is useful in many applications. In this paper, we conduct an
in-depth study to investigate important implementation options that may affect
the performance of deep nets on video classification. Our evaluations are
conducted on top of a recent two-stream convolutional neural network (CNN)
pipeline, which uses both static frames and motion optical flows, and has
demonstrated competitive performance against the state-of-the-art methods. In
order to gain insights and to arrive at a practical guideline, many important
options are studied, including network architectures, model fusion, learning
parameters and the final prediction methods. Based on the evaluations, very
competitive results are attained on two popular video classification
benchmarks. We hope that the discussions and conclusions from this work can
help researchers in related fields to quickly set up a good basis for further
investigations along this very promising direction.Comment: ACM ICMR'1
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201
- …