Trail optimization framework to detect nonlinear object motion in video sequences

2019 ◽  
Vol 14 (3) ◽  
pp. 537-545
Author(s):  
T. Manonmani ◽  
Vijayakumari Pushparaj
Keyword(s):  
Object Motion ◽  
Video Sequences ◽  
Optimization Framework ◽  
Nonlinear Object

MOVING CAMERA MOVING OBJECT SEGMENTATION IN COMPRESSED VIDEO SEQUENCES

2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI
Keyword(s):  
Motion Detection ◽  
Object Segmentation ◽  
Object Motion ◽  
Moving Object ◽  
Transformation Model ◽  
Video Sequences ◽  
Compressed Domain ◽  
Camera Motion ◽  
Consistency Check ◽  
Moving Object Segmentation

In this paper, we address the problem of camera and object motion detection in the compressed domain. The estimation of camera motion and the moving object segmentation have been widely stated in a variety of context for video analysis, due to their capabilities of providing essential clues for interpreting the high-level semantics of video sequences. A novel compressed domain motion estimation and segmentation scheme is presented and applied in this paper. MPEG-2 compressed domain information, namely Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients, is filtered and manipulated to obtain a dense and reliable Motion Vector Field (MVF) over consecutive frames. An iterative segmentation scheme based upon the generalized affine transformation model is exploited to effect the global camera motion detection. The foreground spatiotemporal objects are separated from the background using the temporal consistency check to the output of the iterative segmentation. This consistency check process can coalesce the resulting foreground blocks and weed out unqualified blocks. Illustrative examples are provided to demonstrate the efficacy of the proposed approach.

Learning the Gestalt Rule of Collinearity from Object Motion

2003 ◽  
Vol 15 (8) ◽  
pp. 1865-1896 ◽  
Author(s):  
Carsten Prodöhl ◽  
Rolf P. Würtz ◽  
Christoph von der Malsburg
Keyword(s):  
Visual Experience ◽  
Moving Objects ◽  
A Priori ◽  
Object Motion ◽  
Fundamental Question ◽  
Video Sequences ◽  
Transient Responses ◽  
Self Organized ◽  
The Brain ◽  
Special Case

The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.

scholarly journals Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4484 ◽  
Author(s):  
Víctor García Rubio ◽  
Juan Antonio Rodrigo Ferrán ◽  
Jose Manuel Menéndez García ◽  
Nuria Sánchez Almodóvar ◽  
José María Lalueza Mayordomo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Change Detection ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Slight Modification ◽  
Weather Conditions ◽  
Object Motion ◽  
Security Requirements ◽  
Video Sequences ◽  
Dynamic Background

In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. In this paper we present a system for change detection in video sequences acquired by moving cameras. It is based on the combination of image alignment techniques with a deep learning model based on convolutional neural networks (CNNs). This approach covers two important topics. Firstly, the capability of our system to be adaptable to variations in the UAV flight. In particular, the difference of height between flights, and a slight modification of the camera’s position or movement of the UAV because of natural conditions such as the effect of wind. These modifications can be produced by multiple factors, such as weather conditions, security requirements or human errors. Secondly, the precision of our model to detect changes in diverse environments, which has been compared with state-of-the-art methods in change detection. This has been measured using the Change Detection 2014 dataset, which provides a selection of labelled images from different scenarios for training change detection algorithms. We have used images from dynamic background, intermittent object motion and bad weather sections. These sections have been selected to test our algorithm’s robustness to changes in the background, as in real flight conditions. Our system provides a precise solution for these scenarios, as the mean F-measure score from the image analysis surpasses 97%, and a significant precision in the intermittent object motion category, where the score is above 99%.

scholarly journals Object Modelling and Tracking in Videos via Multidimensional Features

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Zhuhan Jiang
Keyword(s):  
Bayesian Inference ◽  
Object Tracking ◽  
Video Sequence ◽  
Object Motion ◽  
Video Sequences ◽  
Current Frame ◽  
Object Modelling ◽  
Previous Frame ◽  
Object Features ◽  
Probabilistic Location

We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. Consideration has also been made to a number of relevant aspects of object tracking including multidimensional features and the mixture of colours, textures, and object motion. The experiment of the proposed method on the video sequences has been conducted and has shown its effectiveness in capturing the target in a moving background and with nonrigid object motion.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

Analyse des interactions didactiques lors de l’enseignement du football à l’école Tunisienne

2013 ◽  
Vol 22 (5-6) ◽  
pp. 387-404
Author(s):  
Guerchi Maher ◽  
Makram Zghibi
Keyword(s):  
Physical Education ◽  
Qualitative Methodology ◽  
Video Sequences ◽  
Physical Education Teacher ◽  
Education Teacher ◽  
Social Facts ◽  
Close Observation ◽  
The Impact

Abstract Our research focuses on describing what is really happening when a teacher wants to transmit to pupils - girls and boys - knowledge socially marked as masculine. To describe the processes involved in effective didactic interactions between a teacher a pupil and knowledge, we opted for qualitative methodology, consisting on a close observation of the didactic interactions of a teacher with his pupils (girls and boys). Analysis of the interviews focused especially on the nature of knowledge actually transmitted for girls and boys. The studied video sequences permitted to study the didactic interactions more precisely as are actually happening on the pitch. Both tools allowed us to identify the educational intentions of teachers (specialist or not); women or men in the teaching of football. The results show that teachers’ conceptions influence implicitly or explicitly the modalities of their interventions and the nature of football knowledge transmitted to pupils. This makes us think that the impact of social facts (backgrounds) on Tunisian teachers is great. This phenomenon may lock the physical education teacher in some representations modeling masculine and feminine stereotypes and affect his didactic and teaching contribution. Therefore, the teacher must be aware of the impact of the connotation that may have certain “masculine” practices on his interventions and consequently over the pupils learning (either boys or girls).

ОСОБЕННОСТИ АРХИТЕКТУРЫ И ОСНОВНЫЕ ХАРАКТЕРИСТИКИ АППАРАТНОГО ВИДЕОКОДЕКА СТАНДАРТА H.264 В СОСТАВЕ МИКРОСХЕМЫ 1892ВМ14Я

2020 ◽  
Vol 96 (3s) ◽  
pp. 89-96
Author(s):  
А.А. Беляев ◽  
Я.Я. Петричкович ◽  
Т.В. Солохина ◽  
И.А. Беляев
Keyword(s):  
Data Flow ◽  
System On Chip ◽  
Video Codec ◽  
Experimental Results ◽  
Video Sequences ◽  
Ip Core ◽  
Performance Measurements ◽  
Synchronization Mechanism ◽  
Architectural Features ◽  
On Chip

Рассмотрены особенности архитектуры и основные характеристики аппаратного видеокодека по стандарту H.264, входящего в состав микросхемы 1892ВМ14Я (MCom-02). Описан механизм синхронизации потоков данных на основе набора флагов событий. Приведены экспериментальные результаты измерения характеристик производительности разработанного видеокодека на реальных видеосюжетах при различных форматах передаваемого изображения. The paper considers main architectural features and characteristics of H.264 hardware video codec IP-core as a part of MCom- 02 system-on-chip (SoC). Bedides, it presents data flow synchronization mechanism based on event flags set, as well as experimental results of performance measurements for the designed video codec IP-core obtained for different video sequences and different image formats.

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