Adaptive motion estimation based on texture analysis

Seferidis, V.; Ghanbari, M.

doi:10.1109/tcomm.1994.580237

Cited by 22 publications

(18 citation statements)

References 21 publications

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“…While the motion vectors of the player's hand are correctly estimated, the motion vectors for the ball are totally wrong (due to the limited search window of + 8 pixels compared to the large real displacement). The large vertical speed of the ball (almost 46 pixel/frame) could only be estimated by using a more sophisticated motion-estimation algorithm that handles large as well as small motion vectors [24]- [25]. In general, the proposed interpolation method relies on the good estimation of the motion vectors such that its performance increases when better and more accurate motion vectors are used, but drops when they are wrongly estimated.…”

Section: Resultsmentioning

confidence: 99%

Cell-loss concealment in ATM video codecs

Ghanbari

Seferidis

1993

IEEE Trans. Circuits Syst. Video Technol.

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144

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In this paper methods for the interpolation of lost cells in asynchronous-transfer-mode (ATMI networks are studied. It is shown that use of motion-compensated previous frames gives the best results. The quality of the interpolated pictures improves if the motion vectors truly represent the actual motion in the scene. This is only possible with a two-layer coding scheme, where the motion vectors can be delivered to the decoder through the base-layer guaranteed channel. In derivation of the motion vectors at the encoder, use of uncoded input picture frames outperforms the conventional method of motion extraction from the previous coded pictures, despite the lower bit rate of the latter to the former. Depending on the quality of the base layer and the scene activity, the signal-to-noise ratio (SNR) in the cell-loss-interpolated areas can be improved by up to 10 dB.

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Section: Resultsmentioning

confidence: 99%

Cell-loss concealment in ATM video codecs

Ghanbari

Seferidis

1993

IEEE Trans. Circuits Syst. Video Technol.

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144

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“…In this paper, we have taken approach of Khansari et al [14] for search window direction, which estimate the direction of motion of the object to update the location of the search window by using interframe texture analysis technique which is described by Seferidis and Ghanbari [29]. To find the direction the object motion temporal difference histogram [29] of two consecutive frame is used.…”

Section: Algorithm For Search Window Direction For Next Framementioning

confidence: 99%

“…To find the direction the object motion temporal difference histogram [29] of two consecutive frame is used. Coarseness and directionality of the frame difference of two consecutive frames can be computed from temporal difference histogram.…”

Section: Algorithm For Search Window Direction For Next Framementioning

confidence: 99%

Object tracking using combination of daubechies complex wavelet transform and Zernike moment

Khare

Srivastava

Khare

2015

Multimed Tools Appl

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Moving object tracking is one of the challenging problems in computer vision and it has numerous applications in surveillance system, traffic monitoring etc. The goal of object tracking algorithm is to locate a moving object in consecutive video frames. Tracking of moving object in a video becomes difficult due to random motion of objects. This paper introduces a new algorithm for moving object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation property make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas rotation invariance properties of Zernike moment is also helpful for correct object identification in consecutive frames. Therefore combination of these two as feature of object gives better results. The proposed method matches Zernike moments of Daubechies complex wavelet coefficients of object in the first frame to next consecutive frames. The experimental results and performance evaluation parameters validate that the proposed method gives better performance against other state-of-the-art methods.

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“…Thus algorithms which reduce the number of searches for motion estimation, while decreasing the computation, typically produce less accurate MV estimates which increases the bandwidth of the coded video compared with full search ME. Examples of these algorithms include 2D-logarithmic search, three step search, modified conjugate direction search, and hierarchical search [10,15]. Other fast motion estimation algorithms rely on the correlation that typically exists between MVs of spatially and temporally neighboring macroblocks.…”

Section: Reduced Computation Motion Estimation Algorithmsmentioning

confidence: 99%

“…In the hierarchical ME algorithm [15], the image is lowpass filtered and subsampled, possibly several times, to obtain coarser representations of the image. This can be represented by the pyramid structure shown in Figure 2-7.…”

Section: Hierarchical Motion Estimationmentioning

confidence: 99%

Network-driven motion estimation for wireless video terminals

Rabiner

Chandrakasan²

1997

IEEE Trans. Circuits Syst. Video Technol.

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Motion estimation has been shown to help significantly in the compression of video sequences. However, since most motion estimation algorithms require a large amount of computation, it is undesirable to use them in power constrained applications, such as battery operated wireless video terminals. This thesis describes a novel motion estimation algorithm, termed network driven motion estimation, which reduces the power dissipation of wireless video devices in a networked environment by exploiting the predictability of object motion. Since the location of an object in the current frame can be predicted from its location in previous frames, it is possible to optimally partition the motion estimation computation between battery operated portable devices and high powered compute servers on the wired network. In network driven motion estimation, a remote high powered resource at the base-station (or on the wired network), predicts the motion vectors of the current frame from the motion vectors of the previous frames. The base-station then sends these predicted motion vectors to a portable video encoder, where motion compensation proceeds as usual. Network driven motion estimation adaptively adjusts the coding algorithm based on the amount of motion in the sequence, using motion prediction to code portions of the video sequence which contain a large amount of motion and conditional replenishment to code portions of the sequence which contain little scene motion. This algorithm achieves a reduction in the number of operations performed at the encoder for motion estimation by over two orders of magnitude while introducing minimal degradation to the decoded video compared with full search encoder-based motion estimation.

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Adaptive motion estimation based on texture analysis

Cited by 22 publications

References 21 publications

Cell-loss concealment in ATM video codecs

Cell-loss concealment in ATM video codecs

Object tracking using combination of daubechies complex wavelet transform and Zernike moment

Network-driven motion estimation for wireless video terminals

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