8 research outputs found
Crowd Monitoring System
In this time when COVID-19 is expanding quickly, it is imperative to keep a separation from one another and avoid the group in this manner we can diminish the infection spread. Numerous individuals, deliberately or accidentally, gather and meander in the city. Screen every one of these exercises is intense. Our Smart COVID-19 Crowd Detection Camera using Computer Vision Technique will watch out for all exercises and distinguish any group on the spot. The gadget additionally can alarm the concerned authority about unnecessary social affairs. A functioning reconnaissance framework can recognize the distance among people and caution them in this manner we can diminish the spread of dangerous illnesses
Anonymous subject identification and privacy information management in video surveillance
The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework
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Effective evaluation of privacy protection techniques in visible and thermal imagery
Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This paper proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset is made available online for community. We demonstrate effectiveness of proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of proposed method over existing methods
Facial Privacy Protection in Airborne Recreational Videography
PhDCameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for recreational photography and videography. However, aerial photographs and videographs of public places often contain faces of bystanders thus leading to a perceived or actual violation of privacy. To address this issue, this thesis presents a novel privacy lter that adaptively blurs sensitive image regions and is robust against di erent privacy attacks. In particular, the thesis aims to impede face recognition from airborne cameras and explores the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognisable. When individuals are recognisable by facial recognition algorithms, an adaptive ltering mechanism is proposed to lower the face resolution in order to preserve privacy while ensuring a minimum reduction of the delity of the image. Moreover, the lter's parameters are pseudo-randomly changed to make the applied protection robust against di erent privacy attacks. In case of videography, the lter is updated with a motion-dependent temporal smoothing to minimise icker introduced by the pseudo-random switching of the lter's parameters, without compromising on its robustness against di erent privacy attacks. To evaluate the e ciency of the proposed lter, the thesis uses a state-of-the-art face recognition algorithm and synthetically generated face data with 3D geometric image transformations that mimic faces captured from an MAV at di erent heights and pitch angles. For the videography scenario, a small video face data set is rst captured and then the proposed lter is evaluated against di erent privacy attacks and the quality of the resulting video using both objective measures and a subjective test.This work was supported in part by the research initiative Intelligent Vision Austria with funding from the Austrian Federal Ministry of Science, Research and Economy and the Austrian Institute of Technology
Energy Minimization for Multiple Object Tracking
Multiple target tracking aims at reconstructing trajectories of several
moving targets in a dynamic scene, and is of significant relevance for a
large number of applications. For example, predicting a pedestrian’s
action may be employed to warn an inattentive driver and reduce road
accidents; understanding a dynamic environment will facilitate
autonomous robot navigation; and analyzing crowded scenes can prevent
fatalities in mass panics.
The task of multiple target tracking is challenging for various reasons:
First of all, visual data is often ambiguous. For example, the objects
to be tracked can remain undetected due to low contrast and occlusion.
At the same time, background clutter can cause spurious measurements
that distract the tracking algorithm. A second challenge arises when
multiple measurements appear close to one another. Resolving
correspondence ambiguities leads to a combinatorial problem that quickly
becomes more complex with every time step. Moreover, a realistic model
of multi-target tracking should take physical constraints into account.
This is not only important at the level of individual targets but also
regarding interactions between them, which adds to the complexity of the
problem.
In this work the challenges described above are addressed by means of
energy minimization. Given a set of object detections, an energy
function describing the problem at hand is minimized with the goal of
finding a plausible solution for a batch of consecutive frames. Such
offline tracking-by-detection approaches have substantially advanced the
performance of multi-target tracking. Building on these ideas, this
dissertation introduces three novel techniques for multi-target tracking
that extend the state of the art as follows: The first approach
formulates the energy in discrete space, building on the work of Berclaz
et al. (2009). All possible target locations are reduced to a regular
lattice and tracking is posed as an integer linear program (ILP),
enabling (near) global optimality. Unlike prior work, however, the
proposed formulation includes a dynamic model and additional constraints
that enable performing non-maxima suppression (NMS) at the level of
trajectories. These contributions improve the performance both
qualitatively and quantitatively with respect to annotated ground truth.
The second technical contribution is a continuous energy function for
multiple target tracking that overcomes the limitations imposed by
spatial discretization. The continuous formulation is able to capture
important aspects of the problem, such as target localization or motion
estimation, more accurately. More precisely, the data term as well as
all phenomena including mutual exclusion and occlusion, appearance,
dynamics and target persistence are modeled by continuous differentiable
functions. The resulting non-convex optimization problem is minimized
locally by standard conjugate gradient descent in combination with
custom discontinuous jumps. The more accurate representation of the
problem leads to a powerful and robust multi-target tracking approach,
which shows encouraging results on particularly challenging video
sequences.
Both previous methods concentrate on reconstructing trajectories, while
disregarding the target-to-measurement assignment problem. To unify both
data association and trajectory estimation into a single optimization
framework, a discrete-continuous energy is presented in Part III of this
dissertation. Leveraging recent advances in discrete optimization
(Delong et al., 2012), it is possible to formulate multi-target tracking
as a model-fitting approach, where discrete assignments and continuous
trajectory representations are combined into a single objective
function. To enable efficient optimization, the energy is minimized
locally by alternating between the discrete and the continuous set of
variables.
The final contribution of this dissertation is an extensive discussion
on performance evaluation and comparison of tracking algorithms, which
points out important practical issues that ought not be ignored