346 research outputs found
Carried baggage detection and recognition in video surveillance with foreground segmentation
Security cameras installed in public spaces or in private organizations continuously
record video data with the aim of detecting and preventing crime. For that reason,
video content analysis applications, either for real time (i.e. analytic) or post-event
(i.e. forensic) analysis, have gained high interest in recent years. In this thesis,
the primary focus is on two key aspects of video analysis, reliable moving object
segmentation and carried object detection & identification.
A novel moving object segmentation scheme by background subtraction is presented
in this thesis. The scheme relies on background modelling which is based
on multi-directional gradient and phase congruency. As a post processing step,
the detected foreground contours are refined by classifying the edge segments as
either belonging to the foreground or background. Further contour completion
technique by anisotropic diffusion is first introduced in this area. The proposed
method targets cast shadow removal, gradual illumination change invariance, and
closed contour extraction.
A state of the art carried object detection method is employed as a benchmark
algorithm. This method includes silhouette analysis by comparing human temporal
templates with unencumbered human models. The implementation aspects of
the algorithm are improved by automatically estimating the viewing direction of
the pedestrian and are extended by a carried luggage identification module. As
the temporal template is a frequency template and the information that it provides
is not sufficient, a colour temporal template is introduced. The standard
steps followed by the state of the art algorithm are approached from a different
extended (by colour information) perspective, resulting in more accurate carried
object segmentation.
The experiments conducted in this research show that the proposed closed
foreground segmentation technique attains all the aforementioned goals. The incremental
improvements applied to the state of the art carried object detection
algorithm revealed the full potential of the scheme. The experiments demonstrate
the ability of the proposed carried object detection algorithm to supersede the
state of the art method
GAIT Technology for Human Recognition using CNN
Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames
Detecção de eventos complexos em vĂdeos baseada em ritmos visuais
Orientador: HĂ©lio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de eventos complexos em vĂdeos possui várias aplicações práticas relevantes, alavancadas pela grande disponibilidade de câmeras digitais instaladas em aeroportos, estações de Ă´nibus e trens, centros de compras, estádios, hospitais, escolas, prĂ©dios, estradas, entre vários outros locais. Avanços na tecnologia digital tĂŞm aumentado as capacidades dos sistemas em reconhecer eventos em vĂdeos por meio do desenvolvimento de dispositivos com alta resolução, dimensões fĂsicas pequenas e altas taxas de amostragem. Muitos trabalhos disponĂveis na literatura tĂŞm explorado o tema a partir de diferentes pontos de vista. Este trabalho apresenta e avalia uma metodologia para extrair caracterĂsticas dos ritmos visuais no contexto de detecção de eventos em vĂdeos. Um ritmo visual pode ser visto com a projeção de um vĂdeo em uma imagem, tal que a tarefa de análise de vĂdeos Ă© reduzida a um problema de análise de imagens, beneficiando-se de seu baixo custo de processamento em termos de tempo e complexidade. Para demonstrar o potencial do ritmo visual na análise de vĂdeos complexos, trĂŞs problemas da área de visĂŁo computacional sĂŁo selecionados: detecção de eventos anĂ´malos, classificação de ações humanas e reconhecimento de gestos. No primeiro problema, um modelo e? aprendido com situações de normalidade a partir dos rastros deixados pelas pessoas ao andar, enquanto padro?es representativos das ações sĂŁo extraĂdos nos outros dois problemas. Nossa hipo?tese e? de que vĂdeos similares produzem padro?es semelhantes, tal que o problema de classificação de ações pode ser reduzido a uma tarefa de classificação de imagens. Experimentos realizados em bases pĂşblicas de dados demonstram que o mĂ©todo proposto produz resultados promissores com baixo custo de processamento, tornando-o possĂvel aplicar em tempo real. Embora os padro?es dos ritmos visuais sejam extrai?dos como histograma de gradientes, algumas tentativas para adicionar caracterĂsticas do fluxo o?tico sĂŁo discutidas, alĂ©m de estratĂ©gias para obter ritmos visuais alternativosAbstract: The recognition of complex events in videos has currently several important applications, particularly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, hospitals, schools, buildings, roads, among others. Moreover, advances in digital technology have enhanced the capabilities for detection of video events through the development of devices with high resolution, small physical size, and high sampling rates. Many works available in the literature have explored the subject from different perspectives. This work presents and evaluates a methodology for extracting a feature descriptor from visual rhythms of video sequences in order to address the video event detection problem. A visual rhythm can be seen as the projection of a video onto an image, such that the video analysis task can be reduced into an image analysis problem, benefiting from its low processing cost in terms of time and complexity. To demonstrate the potential of the visual rhythm in the analysis of complex videos, three computer vision problems are selected in this work: abnormal event detection, human action classification, and gesture recognition. The former problem learns a normalcy model from the traces that people leave when they walk, whereas the other two problems extract representative patterns from actions. Our hypothesis is that similar videos produce similar patterns, therefore, the action classification problem is reduced into an image classification task. Experiments conducted on well-known public datasets demonstrate that the method produces promising results at high processing rates, making it possible to work in real time. Even though the visual rhythm features are mainly extracted as histogram of gradients, some attempts for adding optical flow features are discussed, as well as strategies for obtaining alternative visual rhythmsMestradoCiĂŞncia da ComputaçãoMestre em CiĂŞncia da Computação1570507, 1406910, 1374943CAPE
Comprehensive review of vision-based fall detection systems
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers
A framework for cardio-pulmonary resuscitation (CPR) scene retrieval from medical simulation videos based on object and activity detection.
In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented Gradients in three dimensions (HOG3D) to retrieve the scenes containing CPR activity. Secondly, we investigate the use of Local Binary Patterns in Three Orthogonal Planes (LBPTOP), which is the three dimensional extension of the popular Local Binary Patterns. This technique is a robust feature that can detect specific activities from scenes containing multiple actors and activities. Thirdly, we propose an improvement to the above mentioned methods by a combination of HOG3D and LBP-TOP. We use decision level fusion techniques to combine the features. We prove experimentally that the proposed techniques and their combination out-perform the existing system for CPR scene retrieval. Finally, we devise a method to detect and retrieve the scenes containing the breathing bag activity, from the medical simulation videos. The proposed framework is tested and validated using eight medical simulation videos and the results are presented
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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
Motion Segmentation from Clustering of Sparse Point Features Using Spatially Constrained Mixture Models
Motion is one of the strongest cues available for segmentation. While motion segmentation finds wide ranging applications in object detection, tracking, surveillance, robotics, image and video compression, scene reconstruction, video editing, and so on, it faces various challenges such as accurate motion recovery from noisy data, varying complexity of the models required to describe the computed image motion, the dynamic nature of the scene that may include a large number of independently moving objects undergoing occlusions, and the need to make high-level decisions while dealing with long image sequences. Keeping the sparse point features as the pivotal point, this thesis presents three distinct approaches that address some of the above mentioned motion segmentation challenges. The first part deals with the detection and tracking of sparse point features in image sequences. A framework is proposed where point features can be tracked jointly. Traditionally, sparse features have been tracked independently of one another. Combining the ideas from Lucas-Kanade and Horn-Schunck, this thesis presents a technique in which the estimated motion of a feature is influenced by the motion of the neighboring features. The joint feature tracking algorithm leads to an improved tracking performance over the standard Lucas-Kanade based tracking approach, especially while tracking features in untextured regions. The second part is related to motion segmentation using sparse point feature trajectories. The approach utilizes a spatially constrained mixture model framework and a greedy EM algorithm to group point features. In contrast to previous work, the algorithm is incremental in nature and allows for an arbitrary number of objects traveling at different relative speeds to be segmented, thus eliminating the need for an explicit initialization of the number of groups. The primary parameter used by the algorithm is the amount of evidence that must be accumulated before the features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. The approach works in real time and is able to segment various challenging sequences captured from still and moving cameras that contain multiple independently moving objects and motion blur. The third part of this thesis deals with the use of specialized models for motion segmentation. The articulated human motion is chosen as a representative example that requires a complex model to be accurately described. A motion-based approach for segmentation, tracking, and pose estimation of articulated bodies is presented. The human body is represented using the trajectories of a number of sparse points. A novel motion descriptor encodes the spatial relationships of the motion vectors representing various parts of the person and can discriminate between articulated and non-articulated motions, as well as between various pose and view angles. Furthermore, a nearest neighbor search for the closest motion descriptor from the labeled training data consisting of the human gait cycle in multiple views is performed, and this distance is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints and scale demonstrate the effectiveness of the approach. In particular, the purely motion based approach is able to track people in night-time sequences, even when the appearance based cues are not available. Finally, an application of image segmentation is presented in the context of iris segmentation. Iris is a widely used biometric for recognition and is known to be highly accurate if the segmentation of the iris region is near perfect. Non-ideal situations arise when the iris undergoes occlusion by eyelashes or eyelids, or the overall quality of the segmented iris is affected by illumination changes, or due to out-of-plane rotation of the eye. The proposed iris segmentation approach combines the appearance and the geometry of the eye to segment iris regions from non-ideal images. The image is modeled as a Markov random field, and a graph cuts based energy minimization algorithm is applied to label the pixels either as eyelashes, pupil, iris, or background using texture and image intensity information. The iris shape is modeled as an ellipse and is used to refine the pixel based segmentation. The results indicate the effectiveness of the segmentation algorithm in handling non-ideal iris images
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