10,490 research outputs found
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Automatic annotation for weakly supervised learning of detectors
PhDObject detection in images and action detection in videos are among the most widely studied
computer vision problems, with applications in consumer photography, surveillance, and automatic
media tagging. Typically, these standard detectors are fully supervised, that is they require
a large body of training data where the locations of the objects/actions in images/videos have
been manually annotated. With the emergence of digital media, and the rise of high-speed internet,
raw images and video are available for little to no cost. However, the manual annotation
of object and action locations remains tedious, slow, and expensive. As a result there has been
a great interest in training detectors with weak supervision where only the presence or absence
of object/action in image/video is needed, not the location. This thesis presents approaches for
weakly supervised learning of object/action detectors with a focus on automatically annotating
object and action locations in images/videos using only binary weak labels indicating the presence
or absence of object/action in images/videos.
First, a framework for weakly supervised learning of object detectors in images is presented.
In the proposed approach, a variation of multiple instance learning (MIL) technique for automatically
annotating object locations in weakly labelled data is presented which, unlike existing
approaches, uses inter-class and intra-class cue fusion to obtain the initial annotation. The initial
annotation is then used to start an iterative process in which standard object detectors are used to
refine the location annotation. Finally, to ensure that the iterative training of detectors do not drift
from the object of interest, a scheme for detecting model drift is also presented. Furthermore,
unlike most other methods, our weakly supervised approach is evaluated on data without manual
pose (object orientation) annotation.
Second, an analysis of the initial annotation of objects, using inter-class and intra-class cues,
is carried out. From the analysis, a new method based on negative mining (NegMine) is presented
for the initial annotation of both object and action data. The NegMine based approach is a
much simpler formulation using only inter-class measure and requires no complex combinatorial
optimisation but can still meet or outperform existing approaches including the previously pre3
sented inter-intra class cue fusion approach. Furthermore, NegMine can be fused with existing
approaches to boost their performance.
Finally, the thesis will take a step back and look at the use of generic object detectors as prior
knowledge in weakly supervised learning of object detectors. These generic object detectors are
typically based on sampling saliency maps that indicate if a pixel belongs to the background
or foreground. A new approach to generating saliency maps is presented that, unlike existing
approaches, looks beyond the current image of interest and into images similar to the current
image. We show that our generic object proposal method can be used by itself to annotate the
weakly labelled object data with surprisingly high accuracy
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
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