10,219 research outputs found
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
Shopping behaviour analysis through counting and tracking of people in
shop-like environments offers valuable information for store operators and
provides key insights in the stores layout (e.g. frequently visited spots).
Instead of using extra staff for this, automated on-premise solutions are
preferred. These automated systems should be cost-effective, preferably on
lightweight embedded hardware, work in very challenging situations (e.g.
handling occlusions) and preferably work real-time. We solve this challenge by
implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector,
on a Jetson TX2 hardware platform. By combining the detector with a sparse
optical flow tracker we assign a unique ID to each customer and tackle the
problem of loosing partially occluded customers. Our detector-tracker based
solution achieves an average precision of 81.59% at a processing speed of 10
FPS. Besides valuable statistics, heat maps of frequently visited spots are
extracted and used as an overlay on the video stream
Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces
Physical retailers, who once led the way in tracking with loyalty cards and
`reverse appends', now lag behind online competitors. Yet we might be seeing
these tables turn, as many increasingly deploy technologies ranging from simple
sensors to advanced emotion detection systems, even enabling them to tailor
prices and shopping experiences on a per-customer basis. Here, we examine these
in-store tracking technologies in the retail context, and evaluate them from
both technical and regulatory standpoints. We first introduce the relevant
technologies in context, before considering privacy impacts, the current
remedies individuals might seek through technology and the law, and those
remedies' limitations. To illustrate challenging tensions in this space we
consider the feasibility of technical and legal approaches to both a) the
recent `Go' store concept from Amazon which requires fine-grained, multi-modal
tracking to function as a shop, and b) current challenges in opting in or out
of increasingly pervasive passive Wi-Fi tracking. The `Go' store presents
significant challenges with its legality in Europe significantly unclear and
unilateral, technical measures to avoid biometric tracking likely ineffective.
In the case of MAC addresses, we see a difficult-to-reconcile clash between
privacy-as-confidentiality and privacy-as-control, and suggest a technical
framework which might help balance the two. Significant challenges exist when
seeking to balance personalisation with privacy, and researchers must work
together, including across the boundaries of preferred privacy definitions, to
come up with solutions that draw on both technology and the legal frameworks to
provide effective and proportionate protection. Retailers, simultaneously, must
ensure that their tracking is not just legal, but worthy of the trust of
concerned data subjects.Comment: 10 pages, 1 figure, Proceedings of the PETRAS/IoTUK/IET Living in the
Internet of Things Conference, London, United Kingdom, 28-29 March 201
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for
the automatic recognition of human actions in the video. Several attempts have
been made to capture frame-level salient aspects through attention but they
lack the capacity to effectively map the temporal relationships in between the
frames as they only capture a limited span of temporal dependencies. To this
end we propose a complete end-to-end supervised learning approach that can
better learn relationships between actions over time, thus improving the
overall segmentation performance. The proposed hierarchical recurrent attention
framework analyses the input video at multiple temporal scales, to form
embeddings at frame level and segment level, and perform fine-grained action
segmentation. This generates a simple, lightweight, yet extremely effective
architecture for segmenting continuous video streams and has multiple
application domains. We evaluate our system on multiple challenging public
benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech
Egocentric datasets, and achieves state-of-the-art performance. The evaluated
datasets encompass numerous video capture settings which are inclusive of
static overhead camera views and dynamic, ego-centric head-mounted camera
views, demonstrating the direct applicability of the proposed framework in a
variety of settings.Comment: Published in Pattern Recognition Letter
Group-In: Group Inference from Wireless Traces of Mobile Devices
This paper proposes Group-In, a wireless scanning system to detect static or
mobile people groups in indoor or outdoor environments. Group-In collects only
wireless traces from the Bluetooth-enabled mobile devices for group inference.
The key problem addressed in this work is to detect not only static groups but
also moving groups with a multi-phased approach based only noisy wireless
Received Signal Strength Indicator (RSSIs) observed by multiple wireless
scanners without localization support. We propose new centralized and
decentralized schemes to process the sparse and noisy wireless data, and
leverage graph-based clustering techniques for group detection from short-term
and long-term aspects. Group-In provides two outcomes: 1) group detection in
short time intervals such as two minutes and 2) long-term linkages such as a
month. To verify the performance, we conduct two experimental studies. One
consists of 27 controlled scenarios in the lab environments. The other is a
real-world scenario where we place Bluetooth scanners in an office environment,
and employees carry beacons for more than one month. Both the controlled and
real-world experiments result in high accuracy group detection in short time
intervals and sampling liberties in terms of the Jaccard index and pairwise
similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under
Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The
content of this paper does not reflect the official opinion of the EU.
Responsibility for the information and views expressed therein lies entirely
with the authors. Proc. of ACM/IEEE IPSN'20, 202
INTELLIGENT VIDEO SURVEILLANCE OF HUMAN MOTION: ANOMALY DETECTION
Intelligent video surveillance is a system that can highlight extraction and
video summarization that require recognition of the activities occurring in the video
without any human supervision. Surveillance systems are extremely helpful to guard
or protect you from any dangerous condition. In this project, we propose a system
that can track and detect abnormal behavior in indoor environment. By concentrating
on inside house enviromnent, we want to detect any abnormal behavior between
adult and toddler to avoid abusing to happen. In general, the frameworks of a video
surveillance system include the following stages: background estimator,
segmentation, detection, tracking, behavior understanding and description. We use
training behavior profile to collect the description and generate statistically behavior
to perform anomaly detection later. We begin with modeling the simplest actions
like: stomping, slapping, kicking, pointed sharp or blunt object that do not require
sophisticated modeling. A method to model actions with more complex dynamic are
then discussed. The results of the system manage to track adult figure, toddler figure
and harm object as third subject. With this system, it can bring attention of human
personnel security. For future work, we recommend to continue design methods for
higher level representation of complex activities to do the matching anomaly
detection with real-time video surveillance. We also propose the system to embed
with hardware solution for triggered the matching detection as output
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