3D Trajectory Extraction from 2D Videos for Human Activity Analysis.

Long description: The present dissertation addresses the problem of extracting 3D trajectories of objects from 2D videos. The reason of this is the theory that these trajectories symbolise high-level interpretations of human activities. A 3D trajectory of an object means its sequential positions in...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Boukhers, Zeyd
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin : Logos Verlag Berlin, 2017.
Colección:Studien Zur Mustererkennung Ser.
Temas:
Image processing > Digital techniques.
Traitement d'images > Techniques numériques.
digital imaging.
Image processing > Digital techniques
Acceso en línea:Texto completo

MARC

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100 1 |a Boukhers, Zeyd. 
245 1 0 |a 3D Trajectory Extraction from 2D Videos for Human Activity Analysis. 
260 |a Berlin :  |b Logos Verlag Berlin,  |c 2017. 
300 |a 1 online resource (160 pages). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Studien Zur Mustererkennung Ser. ;  |v v. 44 
588 0 |a Print version record. 
505 0 |a Intro -- 1 Introduction -- 1.1 Problem -- 1.2 Contribution -- 1.3 Overview -- 2 RelatedWork -- 2.1 Object Detection -- 2.1.1 Segmentation -- 2.1.2 Exhaustive Search -- 2.1.3 Superficial Search -- 2.2 Trajectory Extraction -- 2.2.1 3D Object Tracking -- 2.2.2 3D Trajectory Extraction from Surveillance Videos -- 2.2.3 3D Trajectory Extraction from Moving Cameras -- 2.3 Camera Parameters Estimation -- 2.3.1 Focal Length Estimation -- 2.3.2 Camera Odometry Estimation -- 2.4 Trajectory Analysis -- 2.4.1 Convoy Detection -- 2.4.2 Suspicious Activity Detection -- 3 Object Detection -- 3.1 Object Detection in Surveillance Videos -- 3.1.1 Clustering for One Frame -- 3.1.2 Clustering Improvement for a Frame Sequence -- 3.2 Object Detection in Still Images -- 3.2.1 Systematic Search -- 3.2.2 Classification -- 3.3 Summary -- 4 3D Trajectory Extraction from Surveillance Videos -- 4.1 Initialisation -- 4.2 Best Frame Selection -- 4.3 Particle Filter -- 4.3.1 Transition Distribution -- 4.3.2 Weight Computation -- 4.4 Coordinate Conversion -- 4.5 Summary -- 5 3D Trajectory Extraction from Moving Cameras -- 5.1 Focal Length Estimation -- 5.2 Object's Depth Estimation -- 5.3 Observation Model -- 5.3.1 Camera Observation -- 5.3.2 Object Observation -- 5.4 Motion Model -- 5.4.1 Camera Motion -- 5.4.2 Object Motion -- 5.5 Sampling -- 5.5.1 Object Birth -- 5.5.2 Object Death -- 5.5.3 Object Stay -- 5.5.4 Object Exit -- 5.5.5 Object Update -- 5.5.6 Camera Update -- 5.6 Summary -- 6 Application: Trajectory Analysis -- 6.1 Convoy Detection -- 6.1.1 Noncontinuous Convoy: -- 6.1.2 Clustering and Intersection: -- 6.1.3 Candidate Expiring Mechanism: -- 6.2 Suspicious Activity Detection -- 6.3 Summary -- 7 Experiments and Results -- 7.1 Object Detection -- 7.1.1 Object Detection in Surveillance Videos -- 7.1.2 Object Detection in Still Image -- 7.2 3D Trajectory Extraction. 
505 8 |a 7.2.1 3D Trajectory Extraction from Surveillance Videos -- 7.2.2 3D Trajectory Extraction from Moving Cameras -- 7.3 Camera Parameters Estimation -- 7.3.1 Focal Length Estimation -- 7.3.2 Camera Odometry Estimation -- 7.4 Trajectory Analysis -- 7.4.1 Convoy Detection -- 7.4.2 Suspicious Activity Detection -- 8 Conclusion -- 8.1 Summary -- 8.2 FutureWork -- Abbreviations -- Mathematical Symbols -- List of Figures -- List of Tables -- List of Algorithms -- Bibliography -- Own Publications -- Curriculum Vitae. 
520 |a Long description: The present dissertation addresses the problem of extracting 3D trajectories of objects from 2D videos. The reason of this is the theory that these trajectories symbolise high-level interpretations of human activities. A 3D trajectory of an object means its sequential positions in the real world over time. To this end, a generic framework for detecting objects and extracting their trajectories is proposed. In simpler terms, it means obtaining the 3D coordinate of the objects detected on the image plane and then tracking them in the real world to extract their 3D trajectories. Lastly, this dissertation presents applications of trajectory analysis to understand human activities in crowded environments. In this context, each phase in the framework represents independent approaches dedicated to solving challenging tasks in computer vision and multimedia. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Image processing  |x Digital techniques. 
650 6 |a Traitement d'images  |x Techniques numériques. 
650 7 |a digital imaging.  |2 aat 
650 7 |a Image processing  |x Digital techniques  |2 fast 
758 |i has work:  |a 3D trajectory extraction from 2D videos for human activity analysis (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFGk4WWhjqQWXvkgh68YdP  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Boukhers, Zeyd.  |t 3D Trajectory Extraction from 2D Videos for Human Activity Analysis.  |d Berlin : Logos Verlag Berlin, ©2017  |z 9783832545833 
830 0 |a Studien Zur Mustererkennung Ser. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5216221  |z Texto completo 
938 |a EBL - Ebook Library  |b EBLB  |n EBL5216221 
938 |a YBP Library Services  |b YANK  |n 15138729 
994 |a 92  |b IZTAP 

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