48,398 research outputs found
Living IoT: A Flying Wireless Platform on Live Insects
Sensor networks with devices capable of moving could enable applications
ranging from precision irrigation to environmental sensing. Using mechanical
drones to move sensors, however, severely limits operation time since flight
time is limited by the energy density of current battery technology. We explore
an alternative, biology-based solution: integrate sensing, computing and
communication functionalities onto live flying insects to create a mobile IoT
platform.
Such an approach takes advantage of these tiny, highly efficient biological
insects which are ubiquitous in many outdoor ecosystems, to essentially provide
mobility for free. Doing so however requires addressing key technical
challenges of power, size, weight and self-localization in order for the
insects to perform location-dependent sensing operations as they carry our IoT
payload through the environment. We develop and deploy our platform on
bumblebees which includes backscatter communication, low-power
self-localization hardware, sensors, and a power source. We show that our
platform is capable of sensing, backscattering data at 1 kbps when the insects
are back at the hive, and localizing itself up to distances of 80 m from the
access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang,
In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and
Pattern Recognition Workshops, 201
An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display
We present a tele-immersive system that enables people to interact with each
other in a virtual world using body gestures in addition to verbal
communication. Beyond the obvious applications, including general online
conversations and gaming, we hypothesize that our proposed system would be
particularly beneficial to education by offering rich visual contents and
interactivity. One distinct feature is the integration of egocentric pose
recognition that allows participants to use their gestures to demonstrate and
manipulate virtual objects simultaneously. This functionality enables the
instructor to ef- fectively and efficiently explain and illustrate complex
concepts or sophisticated problems in an intuitive manner. The highly
interactive and flexible environment can capture and sustain more student
attention than the traditional classroom setting and, thus, delivers a
compelling experience to the students. Our main focus here is to investigate
possible solutions for the system design and implementation and devise
strategies for fast, efficient computation suitable for visual data processing
and network transmission. We describe the technique and experiments in details
and provide quantitative performance results, demonstrating our system can be
run comfortably and reliably for different application scenarios. Our
preliminary results are promising and demonstrate the potential for more
compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201
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