6,324 research outputs found
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis
Multiple-object tracking and behavior analysis have been the essential parts
of surveillance video analysis for public security and urban management. With
billions of surveillance video captured all over the world, multiple-object
tracking and behavior analysis by manual labor are cumbersome and cost
expensive. Due to the rapid development of deep learning algorithms in recent
years, automatic object tracking and behavior analysis put forward an urgent
demand on a large scale well-annotated surveillance video dataset that can
reflect the diverse, congested, and complicated scenarios in real applications.
This paper introduces an urban surveillance video dataset (USVD) which is by
far the largest and most comprehensive. The dataset consists of 16 scenes
captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance,
school gate, park, pedestrian mall, and public square. Over 200k video frames
are annotated carefully, resulting in more than 3:7 million object bounding
boxes and about 7:1 thousand trajectories. We further use this dataset to
evaluate the performance of typical algorithms for multiple-object tracking and
anomaly behavior analysis and explore the robustness of these methods in urban
congested scenarios.Comment: 6 pages. This dataset are not available due to the data licens
Crowded Scene Analysis: A Survey
Automated scene analysis has been a topic of great interest in computer
vision and cognitive science. Recently, with the growth of crowd phenomena in
the real world, crowded scene analysis has attracted much attention. However,
the visual occlusions and ambiguities in crowded scenes, as well as the complex
behaviors and scene semantics, make the analysis a challenging task. In the
past few years, an increasing number of works on crowded scene analysis have
been reported, covering different aspects including crowd motion pattern
learning, crowd behavior and activity analysis, and anomaly detection in
crowds. This paper surveys the state-of-the-art techniques on this topic. We
first provide the background knowledge and the available features related to
crowded scenes. Then, existing models, popular algorithms, evaluation
protocols, as well as system performance are provided corresponding to
different aspects of crowded scene analysis. We also outline the available
datasets for performance evaluation. Finally, some research problems and
promising future directions are presented with discussions.Comment: 20 pages in IEEE Transactions on Circuits and Systems for Video
Technology, 201
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal
Abnormal activity recognition requires detection of occurrence of anomalous
events that suffer from a severe imbalance in data. In a video, normal is used
to describe activities that conform to usual events while the irregular events
which do not conform to the normal are referred to as abnormal. It is far more
common to observe normal data than to obtain abnormal data in visual
surveillance. In this paper, we propose an approach where we can obtain
abnormal data by transforming normal data. This is a challenging task that is
solved through a multi-stage pipeline approach. We utilize a number of
techniques from unsupervised segmentation in order to synthesize new samples of
data that are transformed from an existing set of normal examples. Further,
this synthesis approach has useful applications as a data augmentation
technique. An incrementally trained Bayesian convolutional neural network (CNN)
is used to carefully select the set of abnormal samples that can be added.
Finally through this synthesis approach we obtain a comparable set of abnormal
samples that can be used for training the CNN for the classification of normal
vs abnormal samples. We show that this method generalizes to multiple settings
by evaluating it on two real world datasets and achieves improved performance
over other probabilistic techniques that have been used in the past for this
task.Comment: Accepted in IJCNN 201
Implementation of an Onboard Visual Tracking System with Small Unmanned Aerial Vehicle (UAV)
This paper presents a visual tracking system that is capable or running real
time on-board a small UAV (Unmanned Aerial Vehicle). The tracking system is
computationally efficient and invariant to lighting changes and rotation of the
object or the camera. Detection and tracking is autonomously carried out on the
payload computer and there are two different methods for creation of the image
patches. The first method starts detecting and tracking using a stored image
patch created prior to flight with previous flight data. The second method
allows the operator on the ground to select the interest object for the UAV to
track. The tracking system is capable of re-detecting the object of interest in
the events of tracking failure. Performance of the tracking system was verified
both in the lab and during actual flights of the UAV. Results show that the
system can run on-board and track a diverse set of objects in real time.Comment: 9 pages; 6 figures; International Journal of Innovative Technology
and Creative Engineering (ISSN:2045-8711) VOl.1 No. 10 OCTOBER 201
Review on Computer Vision Techniques in Emergency Situation
In emergency situations, actions that save lives and limit the impact of
hazards are crucial. In order to act, situational awareness is needed to decide
what to do. Geolocalized photos and video of the situations as they evolve can
be crucial in better understanding them and making decisions faster. Cameras
are almost everywhere these days, either in terms of smartphones, installed
CCTV cameras, UAVs or others. However, this poses challenges in big data and
information overflow. Moreover, most of the time there are no disasters at any
given location, so humans aiming to detect sudden situations may not be as
alert as needed at any point in time. Consequently, computer vision tools can
be an excellent decision support. The number of emergencies where computer
vision tools has been considered or used is very wide, and there is a great
overlap across related emergency research. Researchers tend to focus on
state-of-the-art systems that cover the same emergency as they are studying,
obviating important research in other fields. In order to unveil this overlap,
the survey is divided along four main axes: the types of emergencies that have
been studied in computer vision, the objective that the algorithms can address,
the type of hardware needed and the algorithms used. Therefore, this review
provides a broad overview of the progress of computer vision covering all sorts
of emergencies.Comment: 25 page
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
This paper presents a novel unsupervised probabilistic model estimation of
visual background in video sequences using a variational autoencoder framework.
Due to the redundant nature of the backgrounds in surveillance videos, visual
information of the background can be compressed into a low-dimensional subspace
in the encoder part of the variational autoencoder, while the highly variant
information of its moving foreground gets filtered throughout its
encoding-decoding process. Our deep probabilistic background model (DeepPBM)
estimation approach is enabled by the power of deep neural networks in learning
compressed representations of video frames and reconstructing them back to the
original domain. We evaluated the performance of our DeepPBM in background
subtraction on 9 surveillance videos from the background model challenge
(BMC2012) dataset, and compared that with a standard subspace learning
technique, robust principle component analysis (RPCA), which similarly
estimates a deterministic low dimensional representation of the background in
videos and is widely used for this application. Our method outperforms RPCA on
BMC2012 dataset with 23% in average in F-measure score, emphasizing that
background subtraction using the trained model can be done in more than 10
times faster
Unsupervised Deep Context Prediction for Background Foreground Separation
In many advanced video based applications background modeling is a
pre-processing step to eliminate redundant data, for instance in tracking or
video surveillance applications. Over the past years background subtraction is
usually based on low level or hand-crafted features such as raw color
components, gradients, or local binary patterns. The background subtraction
algorithms performance suffer in the presence of various challenges such as
dynamic backgrounds, photometric variations, camera jitters, and shadows. To
handle these challenges for the purpose of accurate background modeling we
propose a unified framework based on the algorithm of image inpainting. It is
an unsupervised visual feature learning hybrid Generative Adversarial algorithm
based on context prediction. We have also presented the solution of random
region inpainting by the fusion of center region inpaiting and random region
inpainting with the help of poisson blending technique. Furthermore we also
evaluated foreground object detection with the fusion of our proposed method
and morphological operations. The comparison of our proposed method with 12
state-of-the-art methods shows its stability in the application of background
estimation and foreground detection.Comment: 17 page
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