Scene Change Detection Using Multiple Histograms for Motion-Compensated Frame Rate Up-Conversion

Kang, Suk‐Ju; Cho, Sung In; Yoo, Sungjoo; Kim, Young Hwan

doi:10.1109/jdt.2011.2167740

Cited by 30 publications

(21 citation statements)

References 9 publications

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“…Three benchmark algorithms were used: the classifier-based detection algorithm (Algorithm 1) [ 7 ], the improved Haar feature–based detection algorithm (Algorithm 2) [ 8 , 9 ], and the low binary pattern (LBP)-based detection algorithm (Algorithm 3) [ 6 ]. For an objective evaluation, the proposed algorithm calculated by precision, recall, and F 1 scores [ 14 , 15 ], which are derived as follows:

where TP , FP , and FN denote the number of true positives, false positives, and false negatives that were detected, respectively. Using these values, the F 1 score was calculated, for which a value of one indicates perfect accuracy.…”

Section: Simulation Resultsmentioning

confidence: 99%

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

Kang

2016

Sensors

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This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB) and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

Kang

2016

Sensors

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“…Both local and global histogram performs well in scene change detection process. References [5,6] applied luminance local histogram with directional distribution analysis to local scene change detection, solved the motion blur problem while videos playback on LCDs (Liquid Crystal Displays) by FRUC (Frame Rate Up-Conversion). By using local histogram based scene change detection technique, this algorithm achieved a higher accuracy with less computation compared to other algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…To test our algorithm's performance, we compared our algorithm with two kinds of famous algorithms: a traditional one named Otsu [6] method and a modern one proposed by Kim [12].…”

Section: B Performance Comparisonmentioning

confidence: 99%

A Compressive Sampling Approach to Scene Change Detection

Zhang¹,

Wang

Jin³

et al. 2015

2015 First International Conference on Computational Intelligence Theory, Systems and Applications (CCITSA)

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As an initial step of intelligent video processing, the efficiency of scene change detection algorithm mostly impacts the whole processing progress. Increasing number of researchers has absorbed in improving the algorithm's accuracy or reducing the computational complexity through various approaches. This paper proposes a new approach to scene change detection which explores the high compression efficiency of compressive sampling theory, and only uses 20 compressive samplers each frame to achieve a high scene change detection accuracy. The main contributions of this paper are as follows: we first introduce the compressive sampling theory into video scene change detection technique, then found the best combination of sampler number and the threshold so as to achieve the highest accuracy, and importantly the computational complexity of the proposed algorithm keeps at a low level. The experimental results demonstrate our algorithm's efficiency: 0.93 of precision rate, 1.0 of recall rate and 0.96 of F1 score on average. Meanwhile, comparing with the algorithm with the similar computation complexity, the proposed algorithm is able to improve the detection accuracy by approximate 20%; comparing with the algorithm with the similar detection accuracy, the proposed algorithm can reduce approximate about 40% of computational time.

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“…Kang et al [10] used multiple histograms to perform scene change detection through Otsu thresholding to find out the number of changed blocks in a frame. This method is a fast and efficient technique, but the major drawback here is the manual selection of fixed threshold difference that can cause errors in change detection if not properly set.…”

Section: Related Workmentioning

confidence: 99%

Image Change Detection Using Particle Swarm Optimization

Sagnika

Bilgaiyan

Mishra

2014

Advances in Intelligent Systems and Computing

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Image change detection can be expressed as a function of time period, whose main objective is to find the changes on the same area at different time intervals, which is a complex and intractable one. Due to large search space, general optimization algorithm fails to give the solution in a promising amount of time. So particle swarm optimization (PSO), one of the swarm-based approaches, can be used as an efficient tool, which the authors have explored in this paper. This mechanism aims to find a change mask that performs partitioning of image into changed and unchanged areas so that the weighted sum of mean square errors of both areas is minimized. This leads to accurate change detection with less noise in a feasible time period.

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Scene Change Detection Using Multiple Histograms for Motion-Compensated Frame Rate Up-Conversion

Cited by 30 publications

References 9 publications

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

A Compressive Sampling Approach to Scene Change Detection

Image Change Detection Using Particle Swarm Optimization

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