88,309 research outputs found
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges
Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed
Moving Object Detection and Tracking for Video Surveillance: A Review
This paper presents a review and systematic study on the moving object detection and surveillance of the video as it is an important and challenging task in many computer vision applications, such as human detection, vehicles detection, threat, and security. Video surveillance is a dynamic environment, especially for human and vehicles and for specific object in case of security is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of accidents and crime scene going on now days. The paper also presents the concept of real time implementation computing task in video surveillances system. In this review paper various methods are discussed were evaluation of order to access how well they can detect moving object in an outdoor/indoor section in real time situation
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
Dynamic privacy in a smart house environment
A smart house can be regarded as a surveillance environment in which the person being observed carries out activities that range from intimate to more public. What can be observed depends on the activity, the person observing (e.g. a carer) and policy. In assisted living smart house environments, a single privacy policy, applied throughout, would be either too invasive for an occupant, or too restrictive for an observer, due to the conflicting goals of surveillance and private environments. Hence, we propose a dynamic method for altering the level of privacy in the environment based on the context, the situation within the environment, encompassing factors relevant to ensuring the occupant\u27s safety and privacy. The context is mapped to an appropriate level of privacy, which is implemented by controlling access to data sources (e.g. video) using data hiding techniques. The aim of this work is to decrease the invasiveness of the technology, while retaining the purpose of the system.<br /
Multiple Views Tracking of Maritime Targets
This paper explores techniques for multiple views target tracking in a maritime environment using a mobile surveillance platform. We utilise an omnidirectional camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to estimate the platform?s ego-motion. For each target a part of the omnidirectional video is extracted, forming a corresponding set of virtual cameras. Each target is then tracked using a dynamic template matching method and particle filtering. Its predictions are then used to continuously adjust the orientations of the virtual cameras, keeping a lock on the targets. We demonstrate the performance of the application in several real-world maritime settings
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
Advance video analysis system and its applications
This research aims at developing an Advance Video Analysis System (AVAS) which can be used in wide range of video surveillance applications as well as to detect moving objects and human beings. The AVAS is able to detect and track interested objects along with human. It recognizes activities in an application environment, such as in a room, supermarket, car, or security checkpoint. Designing a real-time video analysis system is a complex task, as many factors including processing speed, system cost, accuracy, and robustness, need to be carefully balanced. This research has focused these factors at two levels, algorithm level and software level. Background elimination algorithm is proposed in this paper to enhance the performance of Smart Camera systems in changing background and varying lighting condition environment. Among the main features of this research some are, Event Id, Video Id, and Human Id which give detail information about the events, videos and other tracked objects. Finally, the software implementation of AVAS is applied to detect motion and then to trigger alarm for the security purposes. The system will trigger alarm once the motion is detected and when it exceeds the desire threshold value it will give warning to prevent any loss or mass destruction. Finally, we have given a number of recommendations that need to be addressed for the future growth of surveillance technologies and meeting the end-users' diversified and dynamic requirements. © EuroJournals Publishing, Inc. 2010
Policy Recognition in the Abstract Hidden Markov Model
In this paper, we present a method for recognising an agent's behaviour in
dynamic, noisy, uncertain domains, and across multiple levels of abstraction.
We term this problem on-line plan recognition under uncertainty and view it
generally as probabilistic inference on the stochastic process representing the
execution of the agent's plan. Our contributions in this paper are twofold. In
terms of probabilistic inference, we introduce the Abstract Hidden Markov Model
(AHMM), a novel type of stochastic processes, provide its dynamic Bayesian
network (DBN) structure and analyse the properties of this network. We then
describe an application of the Rao-Blackwellised Particle Filter to the AHMM
which allows us to construct an efficient, hybrid inference method for this
model. In terms of plan recognition, we propose a novel plan recognition
framework based on the AHMM as the plan execution model. The Rao-Blackwellised
hybrid inference for AHMM can take advantage of the independence properties
inherent in a model of plan execution, leading to an algorithm for online
probabilistic plan recognition that scales well with the number of levels in
the plan hierarchy. This illustrates that while stochastic models for plan
execution can be complex, they exhibit special structures which, if exploited,
can lead to efficient plan recognition algorithms. We demonstrate the
usefulness of the AHMM framework via a behaviour recognition system in a
complex spatial environment using distributed video surveillance data
Inexpensive high dynamic range video for large scale security and surveillance
Abstract-We describe a new method for High Dynamic Range (HDR) Video using alternating exposures that adds no additional cost or bandwidth requirements to individual IP cameras, making it suitable for large scale security and surveillance systems. Sufficient dynamic range is crucial to the efficacy of a surveillance system, as saturated pixels mean a camera can no longer "see" its surrounding environment. High costs associated with hardware for improved dynamic range make them unsuitable for very large networks with hundreds or even thousands of cameras. We outline a scalable software method that uses post-processing to combine the information in adjacent frames of a video sequence captured with alternating short and long exposures. In particular, we introduce a novel bi-directional motion estimation module that utilizes block-based motion vectors to register frames with large differences in global brightness and fast local motion within saturated regions. An HDR post-processing solution can be deployed at a central location to process individual camera streams on an "as needed" basis, removing additional costs at the device-end. Furthermore, cameras continue to transmit low dynamic range frames, so there is no additional bandwidth requirement. Results show significant gains in video quality for inexpensive cameras when exposed to brightness variations common in security and surveillance
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