Gradual Transition Detection Using Average Frame Similarity

Volkmer, Timo; Tahaghoghi, S. M. M.; Williams, Hugh E.

doi:10.1109/cvpr.2004.357

Cited by 13 publications

(14 citation statements)

References 16 publications

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“…Dissolves, fade-in, and fade-out are also discussed in [23], [4], [6]. Volkmer et al [24] have presented a gradual transition detection approach based on the average frame similarity and the adaptive thresholds. Albanese et al [7] have proposed an algorithm which use similarity metric based on the animate vision theory.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Performance Evaluation of Algorithm for Detection of Fades in Video Sequences in Presence of motion and Illumination

Chavan¹,

Akojwar²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract. The detection of a fade in and a fade out in the video segmentation is very difficult as compared to the detection of abrupt transition. Mostly, the disturbances are caused due to the object/camera motion and the illumination arising from flashlight, fire, flicker and explosion. This mainly results into false positives. An algorithm has been proposed for the fade in and fades out detection. In the proposed algorithm, frames are decomposed to 5 levels using db-6 discrete wavelet transform. The mean of approximate wavelet coefficients are calculated for each frame and normalized. The variation in normalized mean of wavelet coefficient against frame index gives the presence of fade in and fades out. In our algorithm, no preprocessing or post processing is required and so the computational complexity is reduced to a great extent. The processing time is very small as compared to the previous techniques. The proposed algorithm detects and identifies almost all the cases of fade in and fade out with a negligible miss and the false detection causing due to illumination and motion. Performance comparison of the proposed algorithm with the other existing techniques clearly demonstrated its effectiveness in terms of performance metrics such as Recall, Precision, F1 measure, RSI and detection rate. Another advantage of our algorithm over the previous ones is that the detection rate is almost 100%.

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Section: Introduction and Related Workmentioning

confidence: 99%

Performance Evaluation of Algorithm for Detection of Fades in Video Sequences in Presence of motion and Illumination

Chavan¹,

Akojwar²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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“…In [36], methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. In [37], a moving query window of frames is maintained and the average frame similarities of the frames of the left side and right side of the center frame are calculated, respectively. Then the ratio of these two similarities is monitored and used to detect gradual transitions.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Shot Change Detection Methods

Banwaskar¹,

Rajurkar²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Shot change detection is a significant step in content based video indexing and retrieval. There are different types of transitions between the shots. Most of the shot change detection algorithms deal with these transitions separately. In this paper, we have carried out the analysis of shot change detection methods like pixel difference, histogram difference and Chi-square test and our proposed method. The proposed shot change detection method is integration of pre-processing and KLT (Kanade-Lucas-Tomasi) corner detection technique. In the pre-processing stage, adaptive local thresholding is used to eliminate non-boundary segments and only candidate segments are retained. The candidate segments are refined using bisection-based comparisons to eliminate non-boundary frames. Only refined candidate segments are preserved for further detections; hence, the speed of shot change detection is improved. KLT corner detection approach is used for obtaining key points in the frames from candidate segments. Shot change is detected if the key points between successive frames are not matching. Experimental results indicate that the proposed method is effective in terms accuracy and also helps in accelerating the shot change detection process, which can lead to better and fast retrieval of video.

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“…Frames on either side of the current frame are then combined into two sets of preand post-frames, and the average similarity of each set to the current frame is determined. We then monitor the ratio between the average similarities, allowing us to detect gradual transitions by observing peaks in the ratio curve [21]. We observed strong results with this technique in 2004, and still good but slightly weaker performance in 2005.…”

Section: Gradual Transitionsmentioning

confidence: 95%

“…This did not exhibit any improvement over the one-pass algorithm that we used in 2004 [21,22]. Consequently, we chose to use our 2004 system this year, but with parameters tuned on the 2005 test set.…”

Section: Shot Boundary Detectionmentioning

confidence: 99%

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Shot Boundary Detection

SpringerReference

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Run overviewWe participated in the shot boundary detection and video search tasks. This page provides a summary of our experiments: Shot Boundary DetectionOur approach uses the moving query window technique [17,18,21,22].We applied the system that we used in 2004 [22] and varied algorithm parameters around the optimal settings that we obtained with training runs on the trecvid 2005 test set.The results of all runs are close together at a high standard in terms of recall and precision, but we could not match the performance that we achieved in previous years. In particular, precision for gradual transition detection has suffered significantly in all runs.We use localised hsv colour histograms with 16 regions and 32 bins per dimension. Our system uses different weights for histogram regions when computing frame differences.We observe decreased performance compared to previous years because of many falsely reported gradual transitions. Cut detection performance suffered due to high brightness levels in most video clips. Some of the fixed thresholds that we use do not allow the algorithm to adapt well to different types of footage. Our system performed best on videos that are similar to the 2004 and 2005 test sets, such as those from cnn or nbc.

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Gradual Transition Detection Using Average Frame Similarity

Cited by 13 publications

References 16 publications

Performance Evaluation of Algorithm for Detection of Fades in Video Sequences in Presence of motion and Illumination

Performance Evaluation of Algorithm for Detection of Fades in Video Sequences in Presence of motion and Illumination

Performance Analysis of Shot Change Detection Methods

Shot Boundary Detection

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