29,170 research outputs found
Autonomous monitoring of cliff nesting seabirds using computer vision
In this paper we describe a proposed system for automatic visual monitoring of seabird populations. Image sequences of cliff face nesting sites are captured using time-lapse digital photography. We are developing image processing software which is designed to automatically interpret these images, determine the number of birds present, and monitor activity. We focus primarily on the the development of low-level image processing techniques to support this goal. We first describe our existing work in video processing, and show how it is suitable for this problem domain. Image samples from a particular nest site are presented, and used to describe the associated challenges. We conclude by showing how we intend to develop our work to construct a distributed system capable of simultaneously monitoring a number of sites in the same locality
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
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, SlepianâWolf and WynerâZiv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
A distributed camera system for multi-resolution surveillance
We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor.
Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database.
Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table.
We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating
under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance
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