22,888 research outputs found
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.Comment: To appear in CVPR 201
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Fusion of Head and Full-Body Detectors for Multi-Object Tracking
In order to track all persons in a scene, the tracking-by-detection paradigm
has proven to be a very effective approach. Yet, relying solely on a single
detector is also a major limitation, as useful image information might be
ignored. Consequently, this work demonstrates how to fuse two detectors into a
tracking system. To obtain the trajectories, we propose to formulate tracking
as a weighted graph labeling problem, resulting in a binary quadratic program.
As such problems are NP-hard, the solution can only be approximated. Based on
the Frank-Wolfe algorithm, we present a new solver that is crucial to handle
such difficult problems. Evaluation on pedestrian tracking is provided for
multiple scenarios, showing superior results over single detector tracking and
standard QP-solvers. Finally, our tracker ranks 2nd on the MOT16 benchmark and
1st on the new MOT17 benchmark, outperforming over 90 trackers.Comment: 10 pages, 4 figures; Winner of the MOT17 challenge; CVPRW 201
Learning Behavioural Context
The original publication is available at www.springerlink.co
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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