3,012 research outputs found
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point
clouds. We propose an encoding of sparse 3D data from the Velodyne sensor
suitable for training a convolutional neural network (CNN). This general
purpose approach is used for segmentation of the sparse point cloud into ground
and non-ground points. The LiDAR data are represented as a multi-channel 2D
signal where the horizontal axis corresponds to the rotation angle and the
vertical axis the indexes channels (i.e. laser beams). Multiple topologies of
relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and
evaluated using a manually annotated dataset we prepared. The results show
significant improvement of performance over the state-of-the-art method by
Zhang et al. in terms of speed and also minor improvements in terms of
accuracy.Comment: ICRA 2018 submissio
Towards Structured Analysis of Broadcast Badminton Videos
Sports video data is recorded for nearly every major tournament but remains
archived and inaccessible to large scale data mining and analytics. It can only
be viewed sequentially or manually tagged with higher-level labels which is
time consuming and prone to errors. In this work, we propose an end-to-end
framework for automatic attributes tagging and analysis of sport videos. We use
commonly available broadcast videos of matches and, unlike previous approaches,
does not rely on special camera setups or additional sensors.
Our focus is on Badminton as the sport of interest. We propose a method to
analyze a large corpus of badminton broadcast videos by segmenting the points
played, tracking and recognizing the players in each point and annotating their
respective badminton strokes. We evaluate the performance on 10 Olympic matches
with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player
detection score ([email protected]), 97.98% player identification accuracy, and stroke
segmentation edit scores of 80.48%. We further show that the automatically
annotated videos alone could enable the gameplay analysis and inference by
computing understandable metrics such as player's reaction time, speed, and
footwork around the court, etc.Comment: 9 page
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