199 research outputs found
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision. Recently, some
deep-learning-based tracking algorithms have been achieving record-breaking
performances. However, due to the high complexity of deep learning, most deep
trackers suffer from low tracking speed, and thus are impractical in many
real-world applications. Some new deep trackers with smaller network structure
achieve high efficiency while at the cost of significant decrease on precision.
In this paper, we propose to transfer the feature for image classification to
the visual tracking domain via convolutional channel reductions. The channel
reduction could be simply viewed as an additional convolutional layer with the
specific task. It not only extracts useful information for object tracking but
also significantly increases the tracking speed. To better accommodate the
useful feature of the target in different scales, the adaptation filters are
designed with different sizes. The yielded visual tracker is real-time and also
illustrates the state-of-the-art accuracies in the experiment involving two
well-adopted benchmarks with more than 100 test videos.Comment: 6 page
Application of Machine Learning Identification and Classification of Muturu and Keteku Cattle Species for a Smart Agricultural Practice in Developing Countries such as Nigeria
Smart technologies have drastically reshaped the traditional methods of practicing agriculture as witnessed in husbandry. In this paper, a novel application of machine learning identification and classification of Muturu and Keteku cattle species in Nigeria was proposed as the mainstream model that enables the precision and intelligence perception of animal husbandry for a smart agricultural practice using enhanced mask region-based convolutional neural networks (mask R-CNN). A performance accuracy of 0.92 mAP (mean Average Precision) was achieved by the enhanced mask R-CNN model, making it on a par with the existing models
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
- …