Threshold-Based Camera Motion Characterization of MPEG Video

Kim, Jae-Gon; Chang, Hyun Sung; Kim, Jinwoong; Kim, Hyung-Myung

doi:10.4218/etrij.04.0203.0009

Cited by 17 publications

(18 citation statements)

References 5 publications

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“…From a parametric motion model, the proposed approach estimates framelevel camera motion, then analyzes segment-level camera motion (on a set of frames). Although the motion estimation used in this work is carried out in uncompressed domain, our method can be adapted to the compressed domain as in [12,11,3]. Indeed, the model parameters which are handled can be indifferently estimated in the compressed or uncompressed domain.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the dominant motion is assumed to come from camera motion. A parametric model is often used to represent this, and the parameters are estimated either in compressed domain [11,3,12] or in uncompressed domain [7,6,8]. Other methods obtain camera motions by directly analyzing the MPEG motion vectors [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Other methods obtain camera motions by directly analyzing the MPEG motion vectors [13][14][15][16]. However, most approaches associate a camera motion type from parameters extracted locally (either between two successive frames or from predicted pictures in MPEG) by using a learning algorithm [13,16], a strategy of thresholding [11,14] or a template-matching algorithm [15]. A stage of filtering is sometimes added to obtain consistent motions [14].…”

Section: Introductionmentioning

confidence: 99%

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A fusion architecture based on TBM for camera motion classification

Guironnet

Pellerin

Rombaut

2007

Image and Vision Computing

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International audienceWe propose in this paper an original method of camera motion classification based on Transferable Belief Model (TBM). It consists in locating in a video the motions of translation and zoom, and the absence of camera motion (i.e static camera). The classification process is based on a rule-based system that is divided into three stages. From a parametric motion model, the first stage consists in combining data to obtain frame-level belief masses on camera motions. To ensure the temporal coherence of motions, a filtering of belief masses according to TBM is achieved. The second stage carries out a separation between static and dynamic frames. In the third stage, a temporal integration allows the motion to be studied on a set of frames and to preserve only those with significant magnitude and duration. Then, a more detailed description of each motion is given. Experimental results obtained show the effectiveness of the method

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fusion architecture based on TBM for camera motion classification

Guironnet

Pellerin

Rombaut

2007

Image and Vision Computing

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“…In [5], the motion vectors field is used as a camera motion representation and the detected motion pattern is classified using support vector machines (SVMs) in one of the following classes: zoom, pan, tilt, and rotation. In [4], [6], and [18], camera motion estimation within video shots is performed in the compressed MPEG video streams, without full frame decompression, using the motion vector fields acquired from the P-and B-video frames. These methods rely on the exploitation of motion vectors distribution or on a few representative global motion parameters.…”

Section: Introductionmentioning

confidence: 99%

Camera Motion Estimation Using a Novel Online Vector Field Model in Particle Filters

Nikitidis

Zafeiriou

Pitas

2008

IEEE Trans. Circuits Syst. Video Technol.

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In this paper, a novel algorithm for parametric camera motion estimation is introduced. More particularly, a novel stochastic vector field model is proposed, which can handle smooth motion patterns derived from long periods of stable camera motion and can also cope with rapid camera motion changes and periods when the camera remains still. The stochastic vector field model is established from a set of noisy measurements, such as motion vectors derived, e.g., from block matching techniques, in order to provide an estimation of the subsequent camera motion in the form of a motion vector field. A set of rules for a robust and online update of the camera motion model parameters is also proposed, based on the expectation maximization algorithm. The proposed model is embedded in a particle filters framework in order to predict the future camera motion based on current and prior observations. We estimate the subsequent camera motion by finding the optimum affine transform parameters so that, when applied to the current video frame, the resulting motion vector field to approximate the one estimated by the stochastic model. Extensive experimental results verify the usefulness of the proposed scheme in camera motion pattern classification and in the accurate estimation of the 2-D affine camera transform motion parameters. Moreover, the camera motion estimation has been incorporated into an object tracker in order to investigate if the new schema improves its tracking efficiency, when camera motion and tracked object motion are combined.Index Terms-Camera motion estimation, expectation maximization (EM) algorithm, particle filtering, vector field model. BASIC NOMENCLATURE FOR THE PROPOSED FRAMEWORKThe unknown state of the dynamical system. The motion vector at the th time (frame) of the th block. ,The observation (in our case a matrix of all the motion vectors ) and the estimate produced by the th particle. The set of observations in a time window between 1 and . The system function that calculates the unknown state of the dynamical system.

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“…Otherwise, a shot may be further decomposed into several subsegments, each of which is considered as an entity undergoing adaptation. Detection of entity boundaries can be easily achieved by computing the content characteristics (for example, camera motion) in the compressed video stream [25]. In practice, there is not a single correct decomposition of entities in a video sequence.…”

Section: Definition Of Adaptation Resource Utility and Their Relationsmentioning

confidence: 99%

An Optimal Framework of Video Adaptation and Its Application to Rate Adaptation Transcoding

Kim¹,

Wang²,

Chang³

et al. 2005

ETRI J

Self Cite

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The adaptation of video according to the heterogeneous and dynamic resource constraints on networks and devices, as well as on user preferences, is a promising approach for universal access and consumption of video content. For optimal adaptation that satisfies the constraints while maximizing the utility that results from the adapted video, it is necessary to devise a systematic way of selecting an appropriate adaptation operation among multiple feasible choices. This paper presents a general conceptual framework that allows the formulation of various adaptations as constrained optimization problems by modeling the relations among feasible adaptation operations, constraints, and utilities. In particular, we present the feasibility of the framework by applying it to a use case of rate adaptation of MPEG-4 video with an explicit modeling of adaptation employing a combination of frame dropping and discrete cosine transform coefficient dropping, constraint, utility, and their mapping relations. Furthermore, we provide a description tool that describes the adaptationconstraint-utility relations as a functional form referred to as a utility function, which has been accepted as a part of the terminal and network quality of service tool in MPEG-21 Digital Item Adaptation (DIA).

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Threshold-Based Camera Motion Characterization of MPEG Video

Cited by 17 publications

References 5 publications

A fusion architecture based on TBM for camera motion classification

A fusion architecture based on TBM for camera motion classification

Camera Motion Estimation Using a Novel Online Vector Field Model in Particle Filters

An Optimal Framework of Video Adaptation and Its Application to Rate Adaptation Transcoding

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