12,421 research outputs found
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
Nonlinear Trajectory Discovery of a Moving Target by Wireless Sensor Networks
Target tracking is an important cooperative sensing application of wireless sensor networks. In these networks energy, computing power and communication bandwidth are scarce. In this paper, we consider a randomly deployed sensor network with sensors acting as a set of distributed datasets. Each dataset is assumed to have its local temporal dataset, along with spatial data and the geographical coordinates of a given object. An approach towards mines global temporal patterns from these datasets and to discovers nonlinear trajectories of a moving object is proposed. It is tested in a simulation environment and compared with straightforward method. The results of the experiments clearly show the benefits of the new approach in terms of energy consumption
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure
Visual motion estimation is an integral and well-studied challenge in
autonomous navigation. Recent work has focused on addressing multimotion
estimation, which is especially challenging in highly dynamic environments.
Such environments not only comprise multiple, complex motions but also tend to
exhibit significant occlusion.
Previous work in object tracking focuses on maintaining the integrity of
object tracks but usually relies on specific appearance-based descriptors or
constrained motion models. These approaches are very effective in specific
applications but do not generalize to the full multimotion estimation problem.
This paper presents a pipeline for estimating multiple motions, including the
camera egomotion, in the presence of occlusions. This approach uses an
expressive motion prior to estimate the SE (3) trajectory of every motion in
the scene, even during temporary occlusions, and identify the reappearance of
motions through motion closure. The performance of this occlusion-robust
multimotion visual odometry (MVO) pipeline is evaluated on real-world data and
the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS). An earlier version of this work first
appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8
pages, 5 figures. Video available at
https://www.youtube.com/watch?v=o_N71AA6FR
Moving Object Trajectories Meta-Model And Spatio-Temporal Queries
In this paper, a general moving object trajectories framework is put forward
to allow independent applications processing trajectories data benefit from a
high level of interoperability, information sharing as well as an efficient
answer for a wide range of complex trajectory queries. Our proposed meta-model
is based on ontology and event approach, incorporates existing presentations of
trajectory and integrates new patterns like space-time path to describe
activities in geographical space-time. We introduce recursive Region of
Interest concepts and deal mobile objects trajectories with diverse
spatio-temporal sampling protocols and different sensors available that
traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4,
No.2, April 201
Cross-layer Optimized Wireless Video Surveillance
A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system.
The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion.
In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality.
The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos.
In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work.
Adviser: Song C
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