33,835 research outputs found
Event-based Face Detection and Tracking in the Blink of an Eye
We present the first purely event-based method for face detection using the
high temporal resolution of an event-based camera. We will rely on a new
feature that has never been used for such a task that relies on detecting eye
blinks. Eye blinks are a unique natural dynamic signature of human faces that
is captured well by event-based sensors that rely on relative changes of
luminance. Although an eye blink can be captured with conventional cameras, we
will show that the dynamics of eye blinks combined with the fact that two eyes
act simultaneously allows to derive a robust methodology for face detection at
a low computational cost and high temporal resolution. We show that eye blinks
have a unique temporal signature over time that can be easily detected by
correlating the acquired local activity with a generic temporal model of eye
blinks that has been generated from a wide population of users. We furthermore
show that once the face is reliably detected it is possible to apply a
probabilistic framework to track the spatial position of a face for each
incoming event while updating the position of trackers. Results are shown for
several indoor and outdoor experiments. We will also release an annotated data
set that can be used for future work on the topic
3D head tracking using normal flow constraints in a vehicle environment
Head tracking is a key component in applications such as human computer interaction, person monitoring, driver monitoring, video conferencing, and object-based compression. The motion of a driver’s head can tell us a lot about his/her mental state; e.g. whether he/she is drowsy, alert, aggressive,
comfortable, tense, distracted, etc. This paper reviews an optical flow based method to track the head pose, both orientation and position, of a person and presents results from real world data recorded in a car environment
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.Comment: 6 pages, under submission to HPEC Graph Challeng
Real-time Spatial Detection and Tracking of Resources in a Construction Environment
Construction accidents with heavy equipment and bad decision making can be based on poor knowledge of the site environment and in both cases may lead to work interruptions and costly delays. Supporting the construction environment with real-time generated three-dimensional (3D) models can help preventing accidents as well as support management by modeling infrastructure assets in 3D. Such models can be integrated in the path planning of construction equipment operations for obstacle avoidance or in a 4D model that simulates construction processes. Detecting and guiding resources, such as personnel, machines and materials in and to the right place on time requires methods and technologies supplying information in real-time. This paper presents research in real-time 3D laser scanning and modeling using high range frame update rate scanning technology. Existing and emerging sensors and techniques in three-dimensional modeling are explained. The presented research successfully developed computational models and algorithms for the real-time detection, tracking, and three-dimensional modeling of static and dynamic construction resources, such as workforce, machines, equipment, and materials based on a 3D video range camera. In particular, the proposed algorithm for rapidly modeling three-dimensional scenes is explained. Laboratory and outdoor field experiments that were conducted to validate the algorithm’s performance and results are discussed
Vision-Based Production of Personalized Video
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
Reproducible Evaluation of Pan-Tilt-Zoom Tracking
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. However, it is very difficult to assess the
progress that has been made on this topic because there is no standard
evaluation methodology. The difficulty in evaluating PTZ tracking algorithms
arises from their dynamic nature. In contrast to other forms of tracking, PTZ
tracking involves both locating the target in the image and controlling the
motors of the camera to aim it so that the target stays in its field of view.
This type of tracking can only be performed online. In this paper, we propose a
new evaluation framework based on a virtual PTZ camera. With this framework,
tracking scenarios do not change for each experiment and we are able to
replicate online PTZ camera control and behavior including camera positioning
delays, tracker processing delays, and numerical zoom. We tested our evaluation
framework with the Camshift tracker to show its viability and to establish
baseline results.Comment: This is an extended version of the 2015 ICIP paper "Reproducible
Evaluation of Pan-Tilt-Zoom Tracking
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
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