46,688 research outputs found
Realtime video survillence and analytics
Providing smart and intelligent security solutions for home as well as large business is a challenging task, in recent this is a currently a booming topic in the IT industry or in almost every area, where today’s challenges are different the end user needs to have a intelligent solution as well as a cheap price to pay. We therefore represent a software system which uses traditional hardware but has new modern features like object recognition, Intrusion detection, video analytics, Real-time video feed over smart phone. It aims at alerting the user as quick as possible so that the user can stop any unwanted activity going on in the video frame or alert the police.it is believed that using a traditional video surveillance systems a lot of unwanted video data is stored and this causes a lot of memory wastage, and even there is no possible way of use of artificial intelligence and video analytics capability. This decreases the citizen security as well as the capability of the system is not fully used. We have a feature of real-time broad casting the live suspicious video feed to the authenticated user by use of mobile devices such as PDA, Smart phone, Tablets so the user can access the video feed anywhere, any time on any device. The user can also turn on and off the notification and some features or even increase the resolution, zoom in and zoom out. The system uses mechanisms such as deep learning, artificial intelligence, and video analytics to perform the above tasks
MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations
Camera orientations (i.e., rotation and zoom) govern the content that a
camera captures in a given scene, which in turn heavily influences the accuracy
of live video analytics pipelines. However, existing analytics approaches leave
this crucial adaptation knob untouched, instead opting to only alter the way
that captured images from fixed orientations are encoded, streamed, and
analyzed. We present MadEye, a camera-server system that automatically and
continually adapts orientations to maximize accuracy for the workload and
resource constraints at hand. To realize this using commodity pan-tilt-zoom
(PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the
massive space of orientations to identify a fruitful subset at each time, and
(2) a novel knowledge distillation strategy to efficiently (with only camera
resources) select the ones that maximize workload accuracy. Experiments on
diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same
resource usage, or achieves the same accuracy with 2-3.7x lower resource costs.Comment: 19 pages, 16 figure
Insights from Analysis of Video Streaming Data to Improve Resource Management
Today a large portion of Internet traffic is video. Over The Top (OTT)
service providers offer video streaming services by creating a large
distributed cloud network on top of a physical infrastructure owned by multiple
entities. Our study explores insights from video streaming activity by
analyzing data collected from Korea's largest OTT service provider. Our
analysis of nationwide data shows interesting characteristics of video
streaming such as correlation between user profile information (e.g., age, sex)
and viewing habits, viewing habits of users (when do the users watch? using
which devices?), viewing patterns (early leaving viewer vs. steady viewer),
etc. Video on Demand (VoD) streaming involves costly (and often limited)
compute, storage, and network resources. Findings from our study will be
beneficial for OTTs, Content Delivery Networks (CDNs), Internet Service
Providers (ISPs), and Carrier Network Operators, to improve their resource
allocation and management techniques.Comment: This is a preprint electronic version of the article accepted to IEEE
CloudNet 201
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
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