Relevant bibliographies by topics / Crowded scenes

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Crowded scenes'

Author: Grafiati
Published: 28 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Crowded scenes"

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Elbishlawi, Sherif, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, and Mostafa M. Mohamed. "Deep Learning-Based Crowd Scene Analysis Survey." Journal of Imaging 6, no. 9 (September 11, 2020): 95. http://dx.doi.org/10.3390/jimaging6090095.

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Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
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Altamimi, A. B., and H. Ullah. "Panic Detection in Crowded Scenes." Engineering, Technology & Applied Science Research 10, no. 2 (April 4, 2020): 5412–18. http://dx.doi.org/10.48084/etasr.3347.

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A crowd is a gathering of a huge number of individuals in a confined area. Early identification and detection of unusual behaviors in terms of panic occurring in crowded scenes are very important. Panic detection comprises of formulating normal scene behaviors and detecting and identifying non-matching behaviors. However, panic detection and recognition is a very difficult problem, especially when considering diverse scenes. Many methods proposed to cope with these problems have limited robustness as the density of the crowd varies. In order to handle this challenge, this paper proposes the integration of different features into a unified model. Discriminant binary patterns and neighborhood information are used to model complex and unique motion patterns in order to characterize different levels of features for diverse types of crowd scenes, focusing in particular on the detection of panic and non-pedestrian entities. The proposed method was evaluated considering two benchmark datasets and outperformed five existing methods.
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Chaudhry, Huma, Mohd Shafry Mohd Rahim, Tanzila Saba, and Amjad Rehman. "Crowd region detection in outdoor scenes using color spaces." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 02 (March 20, 2018): 1850012. http://dx.doi.org/10.1142/s1793962318500125.

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In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.
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Duan, Genquan, Haizhou Ai, Junliang Xing, Song Cao, and Shihong Lao. "Scene Aware Detection and Block Assignment Tracking in crowded scenes." Image and Vision Computing 30, no. 4-5 (May 2012): 292–305. http://dx.doi.org/10.1016/j.imavis.2012.02.008.

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Gnouma, Mariem, Ridha Ejbali, and Mourad Zaied. "Abnormal events’ detection in crowded scenes." Multimedia Tools and Applications 77, no. 19 (February 26, 2018): 24843–64. http://dx.doi.org/10.1007/s11042-018-5701-6.

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Gafni, Niv, and Andrei Sharf. "3D Motion Completion in Crowded Scenes." Computer Graphics Forum 33, no. 5 (August 2014): 65–74. http://dx.doi.org/10.1111/cgf.12432.

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Rho, Seungmin, Wenny Rahayu, and Uyen Trang Nguyen. "Intelligent video surveillance in crowded scenes." Information Fusion 24 (July 2015): 1–2. http://dx.doi.org/10.1016/j.inffus.2014.11.002.

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Conte, Donatello, Pasquale Foggia, Gennaro Percannella, and Mario Vento. "Counting moving persons in crowded scenes." Machine Vision and Applications 24, no. 5 (March 3, 2013): 1029–42. http://dx.doi.org/10.1007/s00138-013-0491-3.

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Chi, Cheng, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, and Xudong Zou. "PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10639–46. http://dx.doi.org/10.1609/aaai.v34i07.6690.

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Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models are available at https://github.com/ChiCheng123/PedHunter.
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Leach, Michael J. V., Ed P. Sparks, and Neil M. Robertson. "Contextual anomaly detection in crowded surveillance scenes." Pattern Recognition Letters 44 (July 2014): 71–79. http://dx.doi.org/10.1016/j.patrec.2013.11.018.

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Dissertations / Theses on the topic "Crowded scenes"

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Ali, Saad. "Taming Crowded Visual Scenes." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3593.

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Computer vision algorithms have played a pivotal role in commercial video surveillance systems for a number of years. However, a common weakness among these systems is their inability to handle crowded scenes. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. We adopt a top-down approach by first performing a global-level analysis that locates dynamically distinct crowd regions within the video. This knowledge is then employed in the detection of abnormal behaviors and tracking of individual targets within crowds. In addition, the thesis explores the utility of contextual information necessary for persistent tracking and re-acquisition of objects in crowded scenes. For the global-level analysis, a framework based on Lagrangian Particle Dynamics is proposed to segment the scene into dynamically distinct crowd regions or groupings. For this purpose, the spatial extent of the video is treated as a phase space of a time-dependent dynamical system in which transport from one region of the phase space to another is controlled by the optical flow. Next, a grid of particles is advected forward in time through the phase space using a numerical integration to generate a "flow map". The flow map relates the initial positions of particles to their final positions. The spatial gradients of the flow map are used to compute a Cauchy Green Deformation tensor that quantifies the amount by which the neighboring particles diverge over the length of the integration. The maximum eigenvalue of the tensor is used to construct a forward Finite Time Lyapunov Exponent (FTLE) field that reveals the Attracting Lagrangian Coherent Structures (LCS). The same process is repeated by advecting the particles backward in time to obtain a backward FTLE field that reveals the repelling LCS. The attracting and repelling LCS are the time dependent invariant manifolds of the phase space and correspond to the boundaries between dynamically distinct crowd flows. The forward and backward FTLE fields are combined to obtain one scalar field that is segmented using a watershed segmentation algorithm to obtain the labeling of distinct crowd-flow segments. Next, abnormal behaviors within the crowd are localized by detecting changes in the number of crowd-flow segments over time. Next, the global-level knowledge of the scene generated by the crowd-flow segmentation is used as an auxiliary source of information for tracking an individual target within a crowd. This is achieved by developing a scene structure-based force model. This force model captures the notion that an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in his or her vicinity. The key ingredients of the force model are three floor fields that are inspired by research in the field of evacuation dynamics; namely, Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of moving from one location to the next by converting the long-range forces into local forces. The SFF specifies regions of the scene that are attractive in nature, such as an exit location. The DFF, which is based on the idea of active walker models, corresponds to the virtual traces created by the movements of nearby individuals in the scene. The BFF specifies influences exhibited by the barriers within the scene, such as walls and no-entry areas. By combining influence from all three fields with the available appearance information, we are able to track individuals in high-density crowds. The results are reported on real-world sequences of marathons and railway stations that contain thousands of people. A comparative analysis with respect to an appearance-based mean shift tracker is also conducted by generating the ground truth. The result of this analysis demonstrates the benefit of using floor fields in crowded scenes. The occurrence of occlusion is very frequent in crowded scenes due to a high number of interacting objects. To overcome this challenge, we propose an algorithm that has been developed to augment a generic tracking algorithm to perform persistent tracking in crowded environments. The algorithm exploits the contextual knowledge, which is divided into two categories consisting of motion context (MC) and appearance context (AC). The MC is a collection of trajectories that are representative of the motion of the occluded or unobserved object. These trajectories belong to other moving individuals in a given environment. The MC is constructed using a clustering scheme based on the Lyapunov Characteristic Exponent (LCE), which measures the mean exponential rate of convergence or divergence of the nearby trajectories in a given state space. Next, the MC is used to predict the location of the occluded or unobserved object in a regression framework. It is important to note that the LCE is used for measuring divergence between a pair of particles while the FTLE field is obtained by computing the LCE for a grid of particles. The appearance context (AC) of a target object consists of its own appearance history and appearance information of the other objects that are occluded. The intent is to make the appearance descriptor of the target object more discriminative with respect to other unobserved objects, thereby reducing the possible confusion between the unobserved objects upon re-acquisition. This is achieved by learning the distribution of the intra-class variation of each occluded object using all of its previous observations. In addition, a distribution of inter-class variation for each target-unobservable object pair is constructed. Finally, the re-acquisition decision is made using both the MC and the AC.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
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Bhatnagar, Deepti S. M. Massachusetts Institute of Technology. "Dropped object detection in crowded scenes." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53204.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 83-85).
In the last decade, the topic of automated surveillance has become very important in the computer vision community. Especially important is the protection of critical transportation places and infrastructure like airport and railway stations. As a step in that direction, we consider the problem of detecting abandoned objects in a crowded scene. Assuming that the scene is being captured through a mid-field static camera, our approach consists of segmenting the foreground from the background and then using a change analyzer to detect any objects which meet certain criteria. In this thesis, we describe a background model and a method of bootstrapping that model in the presence of foreign objects in the foreground. We then use a Markov Random Field formulation to segment the foreground in image frames sampled periodically from the video camera. We use a change analyzer to detect foreground blobs that remain static through the scene and based on certain rules decide if the blob could be a potentially abandoned object.
by Deepti Bhatnagar.
S.M.
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Pathan, Saira Saleem [Verfasser], Bernd [Akademischer Betreuer] Michaelis, and Ayoub [Akademischer Betreuer] Al-Hamadi. "Behavior understanding in non-crowded and crowded scenes / Saira Saleem Pathan. Betreuer: Bernd Michaelis ; Ayoub Al-Hamadi." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1053914083/34.

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Pathan, Saira Saleem Verfasser], Bernd [Akademischer Betreuer] [Michaelis, and Ayoub [Akademischer Betreuer] Al-Hamadi. "Behavior understanding in non-crowded and crowded scenes / Saira Saleem Pathan. Betreuer: Bernd Michaelis ; Ayoub Al-Hamadi." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1053914083/34.

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Wang, Lu, and 王璐. "Three-dimensional model based human detection and tracking in crowded scenes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46587421.

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Tang, Siyu [Verfasser], and Bernt [Akademischer Betreuer] Schiele. "People detection and tracking in crowded scenes / Siyu Tang ; Betreuer: Bernt Schiele." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2017. http://d-nb.info/1142919722/34.

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Simonnet, Damien Remi Jules Joseph. "Detecting and tracking humans in crowded scenes based on 2D image understanding." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/28213/.

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Tracking pedestrians in surveillance videos is an important task, not only in itself but also as a component of pedestrian counting, activity and event recognition, and scene understanding in general. Robust tracking in crowded environments remains a major challenge, mainly due to the occlusions and interactions between pedestrians. Methods to detect humans in a single frame are becoming increasingly accurate. Therefore, the majority of multi-target tracking algorithms in crowds follow a tracking-by-detection approach, along with models of individual and group behaviour, and various types of features to re-identify any given pedestrian (and discriminate them from the remainder). The aim is, given a Closed Circuit TeleVision (CCTV) camera view (moving or static) of a crowded scene, to produce tracks that indicate which pedestrians are entering and leaving the scene to be used in further applications (e.g. a multi-camera tracking scenario). Therefore, this output should be accurate in terms of position, have few false alarms and identity changes (i.e. tracks have not to be fragmented nor switch identity). Consequently, the presented algorithm concentrates on two important characteristics. Firstly, production of a real-time or near real-time output to be practically usable for further applications without penalising the final system. Secondly, management of occlusions which is the main challenge in crowds. The methodology presented, based on a tracking-by-detection approach, proposes an advance over those two aspects through a hierarchical framework to solve short and long occlusions with two novel methods. First, at a fine temporal scale, kinematic features and appearance features based on non-occluded parts are combined to generate short and reliable 'tracklets'. More specifically, this part uses an occlusion map which attributes a local measurement (by searching over the non-occluded parts) to a target without a global measurement (i.e. a measurement generated by the global detector), and demonstrates better results in terms of tracklet length without generating more false alarms or identity changes. Over a longer scale, these tracklets are associated with each other to build up longer tracks for each pedestrian in the scene. This tracklet data association is based on a novel approach that uses dynamic time warping to locate and measure the possible similarities of appearances between tracklets, by varying the time step and phase of the frame-based visual feature. The method, which does not require any target initialisations or camera calibrations, shows significant improvements in terms of false alarms and identity changes, the latter being a critical point for evaluating tracking algorithms. The evaluation framework, based on different metrics introduced in the literature, consists of a set of new track-based metrics (in contrast to frame-based) which enables failure parts of a tracker to be identified and algorithms to be compared as a single value. Finally, advantages of the dual method proposed to solve long and short occlusions are to reduce simultaneously the problem of track fragmentation and identity switches, and to make it naturally extensible to a multi-camera scenario. Results are presented as a tag and track system over a network of moving and static cameras. In addition to public datasets for multi-target tracking in crowds (e.g. Oxford Town Centre (OTC) dataset) where the new methodology introduced (i.e. building tracklets based on non-occluded pedestrian parts plus re-identification with dynamic time warping) shows significant improvements. Two new datasets are introduced to test the robustness of the algorithm proposed in more challenging scenarios. Firstly, a CCTV shopping view centre is used to demonstrate the effectiveness of the algorithm in a more crowded scenario. Secondly, a dataset with a network of CCTV Pan Tilt Zoom (PTZ) cameras tracking a single pedestrian, demonstrates the capability of the algorithm to handle a very difficult scenario (abrupt motion and non-overlapping camera views) and therefore its applicability as a component of a multitarget tracker in a network of static and PTZ cameras. The thesis concludes with a critical analysis of the work and presents future research opportunities (notably the use of this framework in a non-overlapping network of static and PTZ cameras).
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Bažout, David. "Detekce anomálií v chování davu ve video-datech z dronu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445484.

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There have been lots of new drone applications in recent years. Drones are also often used in the field of national security forces. The aim of this work is to design and implement a tool intended for crowd behavior analysis in drone videodata. This tool ensures identification of suspicious behavior of persons and facilitates its localization. The main benefits include the design of a suitable video stabilization algorithm to stabilize small jitters, as well as trace back of the lost scene. Furthermore, two anomaly detectors were proposed, differing in the method of feature vector extraction and background modeling. Compared to the state of the art approaches, they achieved comparable results, but at the same time they brought the possibility of online data processing.
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Mladinovic, Mirjam. "'In order when most out of order' : crowds and crowd scenes in Shakespearean drama." Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569436.

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This thesis investigates the representations of crowds and crowd scenes in Shakespearean drama. Contrary to the assumption that the crowd's character in early modern drama had a peripheral role, this thesis argues that Shakespeare's crowd is a complex "character" in its ,. own right, and that the playwright's use of it in his drama reveals its dramatic importance. / On the stage the crowd was not dangerous because its role was scripted. This study further proposes to view the character of the crowd from a perspective that has not been applied before in reading Shakespeare's drama. It employs Martin Buber's concept 'I-Thou', aiming to demonstrate that Shakespeare's dramatic characters should be perceived as "dramatic items", and examined through their relations, dramatic and theatrical. Furthermore, this thesis introduces the concept of 'the space of the character' which, unlike the term 'character', refers to theatrical relations that shape "dramatic identities" during the theatrical production. This thesis argues that our understanding of the dramatised hero and the crowd is only fully accomplished when we understand, and acknowledge, the relation between them, and that the relation is not only apparent, but inherent to crowd scenes. It is this non-tangible outcome of interaction between staged characters, and the network of these different theatrical relations, that constitutes the 'theatrical' effectiveness of the crowd scene. This thesis further argues that the crowd scenes are always political in nature, and that they focus not only on the interaction between the crowd and the authority figure, but also on the interaction between the stage and the audience. The key point is that the role of the audience in theatre has been widely debated and recognised, and yet the role of crowd scenes has not. This study insists that a crowd scene should be seen as a dramaturgical device or a theatrical trope that utilises the presence of the audience in such a way that no other scene can. It can incorporate the audience in the theatre and simultaneously give them voice on the stage. Through his dramatisation of the character of the crowd Shakespeare reforms our views about crowds. He reminds his audience that the "crowd" is not a many- headed multitude at all times, but that it consists of individuals with different view points. Shakespeare's crowd is thus meaningful and always' in order when most out of order'.
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Lister, Wayne Daniel. "Real-time rendering of animated crowd scenes." Thesis, University of East Anglia, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.551209.

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Simulated crowds can be found in a wide range of real-time applications. Examples include urban planning and cultural heritage visualizations, disaster and military training simulations, through to perhaps most prominently the use of virtual crowds purely for entertainment purposes in the gaming industry. Crowd simulation is very much an interdisciplinary concern and its importance has motivated researchers from a variety of fields; including computer graphics, psychology and robotics. This thesis considers the problem purely from a computer graphics perspective and introduces three new techniques to animate and draw a crowd of virtual humans in real-time. Contribution 1 addresses vertex skinning and begins by noting that for scenes in which many thousands of characters are visualized, it is often the case that individuals are doing much the same thing. A caching system is therefore proposed and used to accelerate the rendering of a crowd by taking advantage of the temporal and intra-crowd coherencies that are inherent within a populated scene. The approach can be considered a geometric interpretation of dynamic impostors and is best suited to low-entropy scenes such as sports fans clapping and cheering in a stadium. Contributions 2 and 3 consider skeletal animation. For performance reasons previous works have relied heavily on pre-computation when animating their crowds but the associated trade- off is control. It is currently far too difficult to make members of a crowd do anything other than play a scripted animation clip and high-level techniques such as inverse kinematics are yet to be fully explored. This thesis describes how a combination of compute shaders and middleware can remove the need for pre-computation and enable a huge library of 'off-the- shelf' animation techniques, not usually available when visualizing a crowd, to be deployed on thousands of crowd members simultaneously.
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Books on the topic "Crowded scenes"

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Crowd scenes: Movies and mass politics. New York: Fordham University Press, 2008.

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Crowd Scenes Movies and Mass Politics. Fordham University Press, 2008. http://dx.doi.org/10.1353/book.66746.

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Tratner, Michael. Crowd Scenes: Movies and Mass Politics. Fordham University Press, 2008.

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STAR CROWD-LOVE SC #3 (Love Scenes, No 3). Ballantine Books, 1987.

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Crossland, Rachel. A Brownian Model for Literary Crowds. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815976.003.0007.

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Chapter 6 applies the ideas explored in Chapter 5 to a range of early twentieth-century literary texts, especially those by Woolf and Lawrence. The focus here is on crowd and city scenes, including the modernist figures of the flâneur and the passante. The chapter as a whole argues for the relevance of contemporary ideas on molecular physics, especially Brownian motion, to portrayals of individual characters in relation to crowds, drawing on a range of texts including Woolf’s Night and Day and Mrs Dalloway, Lawrence’s The Trespasser and The White Peacock, and texts by Joseph Conrad, James Joyce, and H. G. Wells. Together with Chapter 5, this chapter demonstrates how ideas, language, and imagery were shared across disciplines in the early twentieth century, and argues that considering different disciplines together can help us to recapture a sense of the ways in which particular issues were experienced at the time.
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Handbook of Set Design (Crowood Sports Guide). Crowood Press, Limited, The, 2006.

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Rush, Fred. Before the Law. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190461454.003.0003.

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The chapter focuses textually on the embedded parable “Vor dem Gesetz” and the surrounding scene at the Cathedral. It discusses the conception of law at the heart of the parable, its surrounding scene, and The Trial more generally. The author argues that Kafka’s conception of law is actually a collision of two mutually incompatible ways of thinking about and experiencing authority. As to the first, law is experienced as obscure in its demand, and in particular obscure in relation to the modern human expectation that such demands be rational. As to the second, law is experienced as a convergence of contingency that is “necessary” in a very special sense, i.e., that such convergence crowds out space for uniquely human possibility. These aspects are interdependent, the collision typifying Kafka’s approach to the question of what roots humans in both alienation and striving.
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Maslon, Laurence. Was There Too Much of a Crowd, All Too Lush and Loud? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199832538.003.0011.

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The decade of the 1960s provided the last fertile commercial field for pop singers interpreting Broadway material. Songs from incipient Broadway scores were introduced to America far ahead of their debuts on the New York stage; likewise, there was an important cadre of pop singers who were associated with Broadway material: Tony Bennett, Bobby Darin, Louis Armstrong, to name a few. Even more compellingly, there were pop singers who also performed to acclaim on Broadway: Steve Lawrence and Eydie Gormé, Robert Goulet, Sammy Davis, Jr., and Barbra Streisand. The symbiotic relationship between their stage and pop material would invigorate the musical scene. At the same time, rock and roll ascended the cultural ladder and elbowed show music out of the express lane of popular music. Throughout the 1960s, the two genres coexisted in a tenuous détente, but by the end of the decade, Broadway music had to face the specter of cultural irrelevance.
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Gotman, Kélina. Choreomania. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190840419.001.0001.

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This book traces the emergence and spread of the choreomania concept through colonial medical and ethnographic circles, showing how fantasies of instability—and of the Oriental other—haunted scientific modernity. Scenes from the archives of medical history, neurology, psychiatry, sociology, religion, and popular journalism show how the discursive history of the ‘dancing mania’ moved and transformed with its translations throughout the colonial world. From antiquarian references to ancient Greek bacchanals and medieval St. Vitus’s dances, to scientific reperformances of early modern religious ecstasies, and American government anthropology, ‘choreomania’ arose to signal every sort of gestural and choreographic unrest. Village kermesses, revolutionary crowds, and neuromotor disorders—including hysteria, epilepsy, and chorea—were among the many unruly forms of locomotion indiscriminately compared to bacchanalian turmoil. So too, charges of spontaneous political agitation levied against demonstrators from Africa and South America to the South Seas reveal heightened anxieties about the spread of social disorder. Initially employed to describe ‘contagious’ popular dances, jerking movements, and convulsions, with decolonization, the ‘dancing disease’ increasingly described the fitful drama of anti-European revolt. Closely indebted to the work of Michel Foucault, this book opens a new chapter on the way we think epidemic madness and the organization and disorganization of bodies and disciplines in the modern age. Setting ideas about disruptively moving bodies at the heart of the scientific enterprise, this book argues that disciplines themselves were at once more porous and mobile than is commonly allowed, and that ‘dance’ itself has to be radically reimagined across fields.
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Book chapters on the topic "Crowded scenes"

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Boltes, Maik, and Armin Seyfried. "Tracking People in Crowded Scenes." In Pedestrian and Evacuation Dynamics 2012, 533–42. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02447-9_44.

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Yu, Jie, Dirk Farin, and Bernt Schiele. "Multi-target Tracking in Crowded Scenes." In Lecture Notes in Computer Science, 406–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23123-0_41.

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Rodriguez, Mikel, Josef Sivic, and Ivan Laptev. "Analysis of Crowded Scenes in Video." In Intelligent Video Surveillance Systems, 251–72. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577851.ch15.

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Nishino, Ko, and Louis Kratz. "Modeling Crowd Flow for Video Analysis of Crowded Scenes." In Modeling, Simulation and Visual Analysis of Crowds, 237–65. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8483-7_10.

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Li, Juan, Qinglian He, Liya Yang, and Chunfu Shao. "Pedestrian Detection and Counting in Crowded Scenes." In Green Intelligent Transportation Systems, 495–511. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3551-7_39.

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Gnanavel, V. K., and A. Srinivasan. "Abnormal Event Detection in Crowded Video Scenes." In Advances in Intelligent Systems and Computing, 441–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12012-6_48.

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Gnouma, Mariem, Ridha Ejbali, and Mourad Zaied. "Video Anomaly Detection and Localization in Crowded Scenes." In Advances in Intelligent Systems and Computing, 87–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20005-3_9.

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Kim, Byeoung-su, Gwang-Gook Lee, Ja-Young Yoon, Jae-Jun Kim, and Whoi-Yul Kim. "A Method of Counting Pedestrians in Crowded Scenes." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 1117–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_134.

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Mehran, Ramin, Brian E. Moore, and Mubarak Shah. "A Streakline Representation of Flow in Crowded Scenes." In Computer Vision – ECCV 2010, 439–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15558-1_32.

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Zhao, Xuemei, Dian Gong, and Gérard Medioni. "Tracking Using Motion Patterns for Very Crowded Scenes." In Computer Vision – ECCV 2012, 315–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33709-3_23.

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Conference papers on the topic "Crowded scenes"

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Zhao, Jian, Jianshu Li, Yu Cheng, Terence Sim, Shuicheng Yan, and Jiashi Feng. "Understanding Humans in Crowded Scenes." In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240509.

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Rodriguez, Mikel, Saad Ali, and Takeo Kanade. "Tracking in unstructured crowded scenes." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459301.

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Hou, Ya-Li, and Grantham K. H. Pang. "Human detection in crowded scenes." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651982.

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Mahadevan, Vijay, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos. "Anomaly detection in crowded scenes." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539872.

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Orozco, Javier, Shaogang Gong, and Tao Xiang. "Head Pose Classification in Crowded Scenes." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.120.

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Loy, Chen Change, Tao Xiang, and Shaogang Gong. "Salient motion detection in crowded scenes." In 2012 5th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2012. http://dx.doi.org/10.1109/isccsp.2012.6217836.

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Bansal, Akanksha, Gunjan Verma, and Manoj Kumar. "An Optimal Approach to Detect the Human Heads using H-MTF in Crowded Scenes." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.8.

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Abstract:
Due to the increase in the number of people at crowded places leads to some disaster events, there is a necessity to detect the human heads and estimate the crowd density. The counting of the human heads is quite an immense topic in computer vision and digital image processing. This paper focuses on sample frames that are to be extracted from the crowd video UCF_HDDC and S_HOCK datasets. Our proposed Hybridization-Multiple Target Features (H-MTF) method, detects head objects using three prominent features: texture, color, and shape (T, C, and S). With the help of H-MTF, the optimal value can be estimated to detect the exact spot of the head in a crowded place. By applying two evaluation metrics: (i) Average Precision metric (AvP) and (ii) Average Recall metric (AvR), H-MTF has been compared with the existing methods using 2 different datasets. The results are shown in terms of AvP and AvR and our H-MTF method outcomes best from the existing methods.
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Das, Deepan, and Deepak Mishra. "Unsupervised Anomalous Trajectory Detection for Crowded Scenes." In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018. http://dx.doi.org/10.1109/iciinfs.2018.8721320.

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Widhalm, Peter, and Norbert Brandle. "Learning Major Pedestrian Flows in Crowded Scenes." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.988.

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Shuaibu, Aliyu Numi, Aamir Saeed Malik, Ibrahima Faye, and Yasir Salih Ali. "Pedestrian group attributes detection in crowded scenes." In 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, 2017. http://dx.doi.org/10.1109/atsip.2017.8075584.

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Reports on the topic "Crowded scenes"

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Shah, Mubarak, and Haroon Idrees. Taming Crowded Visual Scenes. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada612010.

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Sukthankar, Gita. (YIP-09) Improving Synthesis and Recognition of Crowded Scenes using Statistical Models of Group Behavior. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada578243.

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