Shot boundary detection in the presence of illumination and motion

Warhade, Krishna K.; Merchant, S. N.; Desai, U.B.

doi:10.1007/s11760-011-0262-4

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…For fair comparisons, we determine the action score of each proposal by applying the AS classifier obtained by our best model (trained with all three methods). ( 6) Shot boundary detection methods [5,65,53] detect the change boundaries in the video which can be considered as AS proposals. Then we utilize our best classifier to classify each AS proposal.…”

Section: Results On Thumos'14mentioning

confidence: 99%

Online Detection of Action Start in Untrimmed, Streaming Videos

Shou

Pan

Chan

et al. 2018

Lecture Notes in Computer Science

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We aim to tackle a novel task in action detection -Online Detection of Action Start (ODAS) in untrimmed, streaming videos. The goal of ODAS is to detect the start of an action instance, with high categorization accuracy and low detection latency. ODAS is important in many applications such as early alert generation to allow timely security or emergency response. We propose three novel methods to specifically address the challenges in training ODAS models: (1) hard negative samples generation based on Generative Adversarial Network (GAN) to distinguish ambiguous background, (2) explicitly modeling the temporal consistency between data around action start and data succeeding action start, and (3) adaptive sampling strategy to handle the scarcity of training data. We conduct extensive experiments using THUMOS'14 and ActivityNet. We show that our proposed methods lead to significant performance gains and improve the state-of-the-art methods. An ablation study confirms the effectiveness of each proposed method.

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Section: Results On Thumos'14mentioning

confidence: 99%

Online Detection of Action Start in Untrimmed, Streaming Videos

Shou

Pan

Chan

et al. 2018

Lecture Notes in Computer Science

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“…If the block loss happened in the frame is not the key frame then we can use bidirectional predication and else we restrict the algorithm as a unidirectional prediction. Literature gives multiple algorithms for the video shot boundary detection [8,9]. We use correlation between consecutive frames as a measure for shot boundary detection,…”

Section: Error Concealmentmentioning

confidence: 99%

Novel Approach to Estimate Missing Data Using Spatio-Temporal Estimation Method

Shelotkar¹,

Ingole²

2016

ijacsa

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Abstract-With advancement of wireless technology and the processing power in mobile devices, every handheld device supports numerous video streaming applications. Generally, user datagram protocol (UDP) is used in video transmission technology which does not provide assured quality of service (QoS). Therefore, there is need for video post processing modules for error concealments. In this paper we propose one such algorithm to recover multiple lost blocks of data in video. The proposed algorithm is based on a combination of wavelet transform and spatio-temporal data estimation. We decomposed the frame with lost blocks using wavelet transform in low and high frequency bands. Then the approximate information (low frequency) of missing block is estimated using spatial smoothening and the details (high frequency) are added using bidirectional (temporal) predication of high frequency wavelet coefficients. Finally inverse wavelet transform is applied on modified wavelet coefficients to recover the frame. In proposed algorithm, we carry out an automatic estimation of missing block using spatio-temporal manner. Experiments are carried with different YUV and compressed domain streams. The experimental results show enhancement in PSNR as well as visual quality and cross verified by video quality metrics (VQM).

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“…Roughly speaking, abrupt cuts are more prevailing than gradual transitions in a video which accounts for >99% of all transitions [6]. Again, despite many cut detection algorithms proposed in the literature [7–10], limited efforts have been paid on detecting cuts in the presence of dramatic light variation such as flashlight affecting one frame to several frames, fire effects such as fire affecting a small area to the full frames, flicker with gradually varying intensity and fast/shaky camera/object motion [11–13]. The primary challenge in any cut detection process is a selection of a feature as well as a threshold which could be able to discriminate between variations due to the above‐mentioned effects and variation due to the actual scene change similar to the human visual system.…”

Section: Introductionmentioning

confidence: 99%

Motion and illumination defiant cut detection based on Weber features

Kar

Kanungo²

2018

IET image process

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The spontaneous proliferation of video data necessitates implementing hierarchical structures for various content management applications. Temporal video segmentation is the key towards such management. To address the problem of temporal segmentation, the current communication exploits the concept of psychological behaviour of the human visual system. Towards this goal an abrupt cut detection scheme has been proposed based on Weber's law which provides a strong spatial correlation among the neighbouring pixels. Thus, the authors provide a robust and unique solution for abrupt shot boundary detection when the frames are affected partially or fully by flashlight, fire and flicker, high motion associated with an object or camera. Further, they have devised a model for generating an automatic threshold, taking into account the statistics of the feature vector which quadrates itself with the variation in the contents of the video. The effectiveness of the proposed framework is validated by exhaustive comparison with few contemporary and recent approaches by using benchmark datasets TRECVID 2001, TRECVID 2002, TRECVID 2007 and some publicly available videos. The results obtained give credence to the remarkable improvement in the performance while preserving a good trade‐off between missed hits and false hits as compared to the state‐of‐the‐art methods.

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Shot boundary detection in the presence of illumination and motion

Cited by 13 publications

References 19 publications

Online Detection of Action Start in Untrimmed, Streaming Videos

Online Detection of Action Start in Untrimmed, Streaming Videos

Novel Approach to Estimate Missing Data Using Spatio-Temporal Estimation Method

Motion and illumination defiant cut detection based on Weber features

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