29,058 research outputs found
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
FULL 3D RECONSTRUCTION OF DYNAMIC NON-RIGID SCENES: ACQUISITION AND ENHANCEMENT
Recent advances in commodity depth or 3D sensing technologies have enabled us to move
closer to the goal of accurately sensing and modeling the 3D representations of complex
dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and
e-health, there is now a greater demand for aff ordable and flexible vision systems which
are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D
cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems.
This thesis targets these limitations and builds approaches around commodity 3D
sensing technologies to acquire noise-free and feature preserving full 3D reconstructions
of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view
system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system
composed of several RGB-D cameras covering the whole scene is used. In the first part of
this thesis, the domain of correctly aligning the information acquired from RGB-D cameras
in a multi-view system to provide full and textured 3D reconstructions of dynamic
scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration
problem. This thesis proposes an extrinsic calibration framework which uses the 2D
photometric and 3D geometric information, acquired with RGB-D cameras, according
to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted
bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters
of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic
calibration problem simultaneously. Results show improvement in calibration accuracy
as compared to state-of-art methods. In the second part of this thesis, the domain
of enhancement of noisy and low-resolution 3D data acquired with commodity RGB-D
cameras in both mono-view and multi-view systems is explored. This thesis extends
the state-of-art in mono-view template-free recursive 3D data enhancement which targets
dynamic scenes containing rigid-objects, and thus requires tracking only the global
motions of those objects for view-dependent surface representation and fi ltering. This
thesis proposes to target dynamic scenes containing non-rigid objects which introduces
the complex requirements of tracking relatively large local motions and maintaining data
organization for view-dependent surface representation. The proposed method is shown
to be e ffective in handling non-rigid objects of changing topologies. Building upon the
previous work, this thesis overcomes the requirement of data organization by proposing
an approach based on view-independent surface representation. View-independence
decreases the complexity of the proposed algorithm and allows it the flexibility to process
and enhance noisy data, acquired with multiple cameras in a multi-view system,
simultaneously. Moreover, qualitative and quantitative experimental analysis shows this
method to be more accurate in removing noise to produce enhanced 3D reconstructions
of non-rigid objects. Although, extending this method to a multi-view system would
allow for obtaining instantaneous enhanced full 360 degrees 3D reconstructions of non-rigid
objects, it still lacks the ability to explicitly handle low-resolution data. Therefore, this
thesis proposes a novel recursive dynamic multi-frame 3D super-resolution algorithm
together with a novel 3D bilateral total variation regularization to filter out the noise,
recover details and enhance the resolution of data acquired from commodity cameras in
a multi-view system. Results show that this method is able to build accurate, smooth
and feature preserving full 360 degrees 3D reconstructions of the dynamic scenes containing
non-rigid objects
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
3D Tracking Using Multi-view Based Particle Filters
Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios
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