Quadtree partitioning and extended weighted finite automata-based fractal colour video coding

Kamble, Shailesh D.; Thakur, Nileshsingh V.; Malik, Latesh; Bajaj, Preeti

doi:10.1504/ijim.2016.079115

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Block matching algorithms are widely adopted due to the motion analysis of obcects, tracking the obcects and also in video compression and processing [28] [34]. Block matching motion estimation is used for the video compression and fractal coding [33,34]. The standards for video compression are utilized for the video coding.…”

Section: Related Work On Object Tracking Using Block Matching Algmentioning

confidence: 99%

Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar¹,

Kamble²,

Thakur³

et al. 2017

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

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-Object tracking is one of the vital fields of computer vision that detects the moving object from a video sequence. Object detection is used to detect the object present in the video and to find the exact location of that object. The object tracking can be applied in various fields that include video surveillance, robot vision, traffic monitoring, automated civil or military surveillance system, traffic monitoring, human-computer interaction, vehicle navigation, biomedical image analysis, medical imaging and much more. The object tracking algorithm requires tracking the object in each frame of the video. A common approach is to use the background subtraction, which eliminates the common static background, resulting into foreground region showing the presence of the desired object. Block matching technique is the most popular technique for computing the motion vectors between the two frames of video sequences and different searching techniques are available to compute motion vectors between frames. Still, there is a scope for improvement in modifying or developing a new shape pattern for block matching motion estimation to find out and track the object in the video. This paper presents the several object detection and tracking methods and how block matching can be used to track object from a video.

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Section: Related Work On Object Tracking Using Block Matching Algmentioning

confidence: 99%

Overview of Object Detection and Tracking based on Block Matching Techniques

Samdurkar¹,

Kamble²,

Thakur³

et al. 2017

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

Self Cite

View full text Add to dashboard Cite

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“…Inter-frame compression and Intra-frame compression. Classification of the frame is shown in Figure 1.Video compression [1,2] is a technology that reduces the bandwidth requirement for transmission of video signals.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Block Matching Algorithms for Motion Vector Estimation

Khawase¹,

Kamble²,

Thakur³

et al. 2017

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

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In video compression technique, motion estimation is one of the key components because of its high computation complexity involves in finding the motion vectors (MV) between the frames. The purpose of motion estimation is to reduce the storage space, bandwidth and transmission cost for transmission of video in many multimedia service applications by reducing the temporal redundancies while maintaining a good quality of the video. There are many motion estimation algorithms, but there is a trade-off between algorithms accuracy and speed. Among all of these, block-based motion estimation algorithms are most robust and versatile. In motion estimation, a variety of fast block based matching algorithms has been proposed to address the issues such as reducing the number of search/checkpoints, computational cost, and complexities etc. Due to its simplicity, the block-based technique is most popular. Motion estimation is only known for video coding process but for solving real life applications many researchers from the different domain are attracted towards block matching algorithms for motion vector estimation.This paper is a review of various block matching algorithms based on shapes and patterns as well as block matching criteria used for motion estimation.

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“…Estimation of Motion vector (MV) by reducing the size of the blocks can also make the tracking process fast [20]. The various applications of block matching algorithm other than tracking are used in video compression [21], block matching motion estimation using automata theory for fractal coding [22,23] and also modified Three-Step Search (TSS) algorithm using fractal coding [24]. The various sections of this paper are organized as follows: Algorithm and analysis of DS and CDS are given in Section II.…”

mentioning

confidence: 99%

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Samdurkar¹,

Kamble²,

Thakur³

et al. 2018

IJIMAI

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Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS) and cross diamond search algorithms (CDS) are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS) algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS) in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time.

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Quadtree partitioning and extended weighted finite automata-based fractal colour video coding

Cited by 7 publications

References 22 publications

Overview of Object Detection and Tracking based on Block Matching Techniques

Overview of Object Detection and Tracking based on Block Matching Techniques

An Overview of Block Matching Algorithms for Motion Vector Estimation

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

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