1,146 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments
Traditionally, recognition systems were only based on human hard biometrics. However,
the ubiquitous CCTV cameras have raised the desire to analyze human biometrics from
far distances, without people attendance in the acquisition process. Highresolution
face closeshots
are rarely available at far distances such that facebased
systems cannot
provide reliable results in surveillance applications. Human soft biometrics such as body
and clothing attributes are believed to be more effective in analyzing human data collected
by security cameras.
This thesis contributes to the human soft biometric analysis in uncontrolled environments
and mainly focuses on two tasks: Pedestrian Attribute Recognition (PAR) and person reidentification
(reid).
We first review the literature of both tasks and highlight the history
of advancements, recent developments, and the existing benchmarks. PAR and person reid
difficulties are due to significant distances between intraclass
samples, which originate
from variations in several factors such as body pose, illumination, background, occlusion,
and data resolution. Recent stateoftheart
approaches present endtoend
models that
can extract discriminative and comprehensive feature representations from people. The
correlation between different regions of the body and dealing with limited learning data
is also the objective of many recent works. Moreover, class imbalance and correlation
between human attributes are specific challenges associated with the PAR problem.
We collect a large surveillance dataset to train a novel gender recognition model suitable
for uncontrolled environments. We propose a deep residual network that extracts several
posewise
patches from samples and obtains a comprehensive feature representation. In
the next step, we develop a model for multiple attribute recognition at once. Considering
the correlation between human semantic attributes and class imbalance, we respectively
use a multitask
model and a weighted loss function. We also propose a multiplication
layer on top of the backbone features extraction layers to exclude the background features
from the final representation of samples and draw the attention of the model to the
foreground area.
We address the problem of person reid
by implicitly defining the receptive fields of
deep learning classification frameworks. The receptive fields of deep learning models
determine the most significant regions of the input data for providing correct decisions.
Therefore, we synthesize a set of learning data in which the destructive regions (e.g.,
background) in each pair of instances are interchanged. A segmentation module
determines destructive and useful regions in each sample, and the label of synthesized
instances are inherited from the sample that shared the useful regions in the synthesized
image. The synthesized learning data are then used in the learning phase and help
the model rapidly learn that the identity and background regions are not correlated.
Meanwhile, the proposed solution could be seen as a data augmentation approach that
fully preserves the label information and is compatible with other data augmentation
techniques.
When reid
methods are learned in scenarios where the target person appears with identical garments in the gallery, the visual appearance of clothes is given the most
importance in the final feature representation. Clothbased
representations are not
reliable in the longterm
reid
settings as people may change their clothes. Therefore,
developing solutions that ignore clothing cues and focus on identityrelevant
features are
in demand. We transform the original data such that the identityrelevant
information of
people (e.g., face and body shape) are removed, while the identityunrelated
cues (i.e.,
color and texture of clothes) remain unchanged. A learned model on the synthesized
dataset predicts the identityunrelated
cues (shortterm
features). Therefore, we train a
second model coupled with the first model and learns the embeddings of the original data
such that the similarity between the embeddings of the original and synthesized data is
minimized. This way, the second model predicts based on the identityrelated
(longterm)
representation of people.
To evaluate the performance of the proposed models, we use PAR and person reid
datasets, namely BIODI, PETA, RAP, Market1501,
MSMTV2,
PRCC, LTCC, and MIT
and compared our experimental results with stateoftheart
methods in the field.
In conclusion, the data collected from surveillance cameras have low resolution, such
that the extraction of hard biometric features is not possible, and facebased
approaches
produce poor results. In contrast, soft biometrics are robust to variations in data quality.
So, we propose approaches both for PAR and person reid
to learn discriminative features
from each instance and evaluate our proposed solutions on several publicly available
benchmarks.This thesis was prepared at the University of Beria Interior, IT Instituto de Telecomunicações, Soft Computing and Image Analysis Laboratory (SOCIA Lab), Covilhã Delegation, and was submitted to the University of Beira Interior for defense in a public examination session
Object detection, recognition and re-identification in video footage
There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis.
A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service.
A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique.
For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images.
Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification
Re-identification and semantic retrieval of pedestrians in video surveillance scenarios
Person re-identification consists of recognizing individuals across different sensors of a camera
network. Whereas clothing appearance cues are widely used, other modalities could
be exploited as additional information sources, like anthropometric measures and gait. In
this work we investigate whether the re-identification accuracy of clothing appearance descriptors
can be improved by fusing them with anthropometric measures extracted from
depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased
framework for building and fusing multi-modal descriptors of pedestrian images for
re-identification tasks, as an alternative to the widely used score-level fusion. The experimental
evaluation is carried out on two data sets including RGB-D data, one of which is a
novel, publicly available data set that we acquired using Kinect sensors.
In this dissertation we also consider a related task, named semantic retrieval of pedestrians
in video surveillance scenarios, which consists of searching images of individuals using
a textual description of clothing appearance as a query, given by a Boolean combination of
predefined attributes. This can be useful in applications like forensic video analysis, where
the query can be obtained froma eyewitness report. We propose a general method for implementing
semantic retrieval as an extension of a given re-identification system that uses any
multiple part-multiple component appearance descriptor. Additionally, we investigate on
deep learning techniques to improve both the accuracy of attribute detectors and generalization
capabilities. Finally, we experimentally evaluate our methods on several benchmark
datasets originally built for re-identification task
From clothing to identity; manual and automatic soft biometrics
Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re identification either to supplement soft body traits or to be used alone. The automatically- and manually-derived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification
Cloth-changing person reidentification (ReID) is a newly emerging research
topic that is aimed at addressing the issues of large feature variations due to
cloth-changing and pedestrian view/pose changes. Although significant progress
has been achieved by introducing extra information (e.g., human contour
sketching information, human body keypoints, and 3D human information),
cloth-changing person ReID is still challenging due to impressionable
pedestrian representations. Moreover, human semantic information and pedestrian
identity information are not fully explored. To solve these issues, we propose
a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing
person ReID, where the human semantic is fully utilized and the identity is
unchangeable to guide collaborative learning. First, we design a novel clothing
attention degradation stream to reasonably reduce the interference caused by
clothing information where clothing attention and mid-level collaborative
learning are employed. Second, we propose a human semantic attention and body
jigsaw stream to highlight the human semantic information and simulate
different poses of the same identity. In this way, the extraction features not
only focus on human semantic information that is unrelated to the background
but also are suitable for pedestrian pose variations. Moreover, a pedestrian
identity enhancement stream is further proposed to enhance the identity
importance and extract more favorable identity robust features. Most
importantly, all these streams are jointly explored in an end-to-end unified
framework, and the identity is utilized to guide the optimization. Extensive
experiments on five public clothing person ReID datasets demonstrate that the
proposed IGCL significantly outperforms SOTA methods and that the extracted
feature is more robust, discriminative, and clothing-irrelevant
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
Deep person generation has attracted extensive research attention due to its
wide applications in virtual agents, video conferencing, online shopping and
art/movie production. With the advancement of deep learning, visual appearances
(face, pose, cloth) of a person image can be easily generated or manipulated on
demand. In this survey, we first summarize the scope of person generation, and
then systematically review recent progress and technical trends in deep person
generation, covering three major tasks: talking-head generation (face),
pose-guided person generation (pose) and garment-oriented person generation
(cloth). More than two hundred papers are covered for a thorough overview, and
the milestone works are highlighted to witness the major technical
breakthrough. Based on these fundamental tasks, a number of applications are
investigated, e.g., virtual fitting, digital human, generative data
augmentation. We hope this survey could shed some light on the future prospects
of deep person generation, and provide a helpful foundation for full
applications towards digital human
Robust pedestrian detection and path prediction using mmproved YOLOv5
In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges due to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study
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