5 research outputs found

    Visual detection and tracking of unknown number of objects with nonparametric clustering methods

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    Clustering methods that do not expect the number of clusters to be known a priori and infer the number of clusters are known as nonparametric clustering methods in the literature. In this thesis we propose novel approaches to common computer vision applications using nonparametric clustering. We attack the problems of multiple object tracking and people counting. Our main motivation is to approach those as data association tasks where we de ne the data association problem speci c to the nature of the application and bene t from the nonparametric nature of the clustering model. We rst propose a detection free tracking method which tracks an unknown number of objects by clustering superpixels. We de ne the clusters as targets with spatial and visual features and track their changes through time by sequential clustering. The clusters yield tracked targets through time. We also propose a method for clustering short track segments into unknown number of tracks. The clustering similarity is de ned using the spatio-temporal features of the short track segments. The clustering process yields robust tracks of objects through time. We use this approach also to improve the tracking results of the detection free tracking proposed before. Finally we cluster raw person detector outputs to obtain groups of people in a scene and estimate the number of people inside a cluster using the features already extracted for clustering with a proposed metric which is invariant to perspective distortion

    Generic multiple object tracking

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    Multiple object tracking is an important problem in the computer vision community due to its applications, including but not limited to, visual surveillance, crowd behavior analysis and robotics. The difficulties of this problem lie in several challenges such as frequent occlusion, interaction, high-degree articulation, etc. In recent years, data association based approaches have been successful in tracking multiple pedestrians on top of specific kinds of object detectors. Thus these approaches are type-specific. This may constrain their application in scenario where type-specific object detectors are unavailable. In view of this, I investigate in this thesis tracking multiple objects without ready-to-use and type-specific object detectors. More specifically, the problem of multiple object tracking is generalized to tracking targets of a generic type. Namely, objects to be tracked are no longer constrained to be a specific kind of objects. This problem is termed as Generic Multiple Object Tracking (GMOT), which is handled by three approaches presented in this thesis. In the first approach, a generic object detector is learned based on manual annotation of only one initial bounding box. Then the detector is employed to regularize the online learning procedure of multiple trackers which are specialized to each object. More specifically, multiple trackers are learned simultaneously with shared features and are guided to keep close to the detector. Experimental results have shown considerable improvement on this problem compared with the state-of-the-art methods. The second approach treats detection and tracking of multiple generic objects as a bi-label propagation procedure, which is consisted of class label propagation (detection) and object label propagation (tracking). In particular, the cluster Multiple Task Learning (cMTL) is employed along with the spatio-temporal consistency to address the online detection problem. The tracking problem is addressed by associating existing trajectories with new detection responses considering appearance, motion and context information. The advantages of this approach is verified by extensive experiments on several public data sets. The aforementioned two approaches handle GMOT in an online manner. In contrast, a batch method is proposed in the third work. It dynamically clusters given detection hypotheses into groups corresponding to individual objects. Inspired by the success of topic model in tackling textual tasks, Dirichlet Process Mixture Model (DPMM) is utilized to address the tracking problem by cooperating with the so-called must-links and cannot-links, which are proposed to avoid physical collision. Moreover, two kinds of representations, superpixel and Deformable Part Model (DPM), are introduced to track both rigid and non-rigid objects. Effectiveness of the proposed method is demonstrated with experiments on public data sets.Open Acces

    Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments

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    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

    Program and Proceedings: The Nebraska Academy of Sciences 1880-2023. 142th Anniversary Year. One Hundred-Thirty-Third Annual Meeting April 21, 2023. Hybrid Meeting: Nebraska Wesleyan University & Online, Lincoln, Nebraska

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    AERONAUTICS & SPACE SCIENCE Chairperson(s): Dr. Scott Tarry & Michaela Lucas HUMANS PAST AND PRESENT Chairperson(s): Phil R. Geib & Allegra Ward APPLIED SCIENCE & TECHNOLOGY SECTION Chairperson(s): Mary Ettel BIOLOGY Chairpersons: Lauren Gillespie, Steve Heinisch, and Paul Davis BIOMEDICAL SCIENCES Chairperson(s): Annemarie Shibata, Kimberly Carlson, Joseph Dolence, Alexis Hobbs, James Fletcher, Paul Denton CHEM Section Chairperson(s): Nathanael Fackler EARTH SCIENCES Chairpersons: Irina Filina, Jon Schueth, Ross Dixon, Michael Leite ENVIRONMENTAL SCIENCE Chairperson: Mark Hammer PHYSICS Chairperson(s): Dr. Adam Davis SCIENCE EDUCATION Chairperson: Christine Gustafson 2023 Maiben Lecturer: Jason Bartz 2023 FRIEND OF SCIENCE AWARD TO: Ray Ward and Jim Lewi
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