1,238 research outputs found
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/
3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures
Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
Human Pose Estimation with Implicit Shape Models
This work presents a new approach for estimating 3D human poses based on monocular camera information only. For this, the Implicit Shape Model is augmented by new voting strategies that allow to localize 2D anatomical landmarks in the image. The actual 3D pose estimation is then formulated as a Particle Swarm Optimization (PSO) where projected 3D pose hypotheses are compared with the generated landmark vote distributions
Real-Time Body Pose Recognition Using 2D or 3D Haarlets
This article presents a novel approach to markerless real-time pose recognition in a multicamera setup. Body pose is retrieved using example-based classification based on Haar wavelet-like features to allow for real-time pose recognition. Average Neighborhood Margin Maximization (ANMM) is introduced as a powerful new technique to train Haar-like features. The rotation invariant approach is implemented for both 2D classification based on silhouettes, and 3D classification based on visual hull
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Video content analysis for automated detection and tracking of humans in CCTV surveillance applications
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented
Adjustable Method Based on Body Parts for Improving the Accuracy of 3D Reconstruction in Visually Important Body Parts from Silhouettes
This research proposes a novel adjustable algorithm for reconstructing 3D
body shapes from front and side silhouettes. Most recent silhouette-based
approaches use a deep neural network trained by silhouettes and key points to
estimate the shape parameters but cannot accurately fit the model to the body
contours and consequently are struggling to cover detailed body geometry,
especially in the torso. In addition, in most of these cases, body parts have
the same accuracy priority, making the optimization harder and avoiding
reaching the optimum possible result in essential body parts, like the torso,
which is visually important in most applications, such as virtual garment
fitting. In the proposed method, we can adjust the expected accuracy for each
body part based on our purpose by assigning coefficients for the distance of
each body part between the projected 3D body and 2D silhouettes. To measure
this distance, we first recognize the correspondent body parts using body
segmentation in both views. Then, we align individual body parts by 2D rigid
registration and match them using pairwise matching. The objective function
tries to minimize the distance cost for the individual body parts in both views
based on distances and coefficients by optimizing the statistical model
parameters. We also handle the slight variation in the degree of arms and limbs
by matching the pose. We evaluate the proposed method with synthetic body
meshes from the normalized S-SCAPE. The result shows that the algorithm can
more accurately reconstruct visually important body parts with high
coefficients.Comment: 16 pages, 17 image
Video object tracking : contributions to object description and performance assessment
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201
Robust surface modelling of visual hull from multiple silhouettes
Reconstructing depth information from images is one of the actively researched themes
in computer vision and its application involves most vision research areas from object
recognition to realistic visualisation. Amongst other useful vision-based reconstruction
techniques, this thesis extensively investigates the visual hull (VH) concept for volume
approximation and its robust surface modelling when various views of an object are
available. Assuming that multiple images are captured from a circular motion, projection
matrices are generally parameterised in terms of a rotation angle from a reference position
in order to facilitate the multi-camera calibration. However, this assumption is often
violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle
is hardly realisable. To address this problem, at first, this thesis proposes a calibration
method associated with the approximate circular motion.
With these modified projection matrices, a resulting VH is represented by a hierarchical
tree structure of voxels from which surfaces are extracted by the Marching
cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by
a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and
imperfect image processing or calibration result. To avoid this sensitivity, this thesis
proposes a robust surface construction algorithm which initially classifies local convex
regions from imperfect MC vertices and then aggregates local surfaces constructed by the
3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline
images to refine a coarse VH using an affine invariant region descriptor. This improves
the quality of VH when a small number of initial views is given.
In conclusion, the proposed methods achieve a 3D model with enhanced accuracy.
Also, robust surface modelling is retained when silhouette images are degraded by
practical noise
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