Advances in Video Summarization and Skimming

Jiang, Richard; Sadka, A.H.; Crookes, Danny

doi:10.1007/978-3-642-02900-4_2

Cited by 24 publications

(12 citation statements)

References 45 publications

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“…Consequently, in order to prevent traffic jams, accidents, or parking problems, intelligent traffic systems are becoming essential on city streets and highways. LPR is one of the most important computer vision techniques in videobased traffic surveillance systems [1], [2]. In particular, LPR provides crucial information about the involved vehicles.…”

Section: Introductionmentioning

confidence: 99%

License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change

Kim

Han³

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

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Section: Introductionmentioning

confidence: 99%

License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change

Kim

Han³

et al. 2017

IEICE Trans. Inf. & Syst.

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“…Various visual features have been used for evaluating frame difference such as color histograms and frame correlations [2]. DeMenthon et al [3] extracted the key frames by finding discontinuities on a trajectory curve, which represent a video sequence.…”

Section: Introductionmentioning

confidence: 99%

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Song

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYKey frame based video summarization has emerged as an important task for efficient video data management. This paper proposes a novel technique for key frame extraction based on chaos theory and color information. By applying chaos theory, a large content change between frames becomes more chaos-like and results in a more complex fractal trajectory in phase space. By exploiting the fractality measured in the phase space between frames, it is possible to evaluate inter-frame content changes invariant to effects of fades and illumination change. In addition to this measure, the color histogram-based measure is also used to complement the chaos-based measure which is sensitive to changes of camera /object motion. By comparing the last key frame with the current frame based on the proposed frame difference measure combining these two complementary measures, the key frames are robustly selected even under presence of video fades, changes of illumination, and camera/object motion. The experimental results demonstrate its effectiveness with significant improvement over the conventional method.

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“…While offline summarization can remain an ideal solution for applications in content based video retrieval, indexing, visualization, etc., an online version is critical for visual surveillance, compression and recognition [10]. From another perspective, video summarization technique has also been classified into skimming [12], montages [13], synopsis [21], etc. However, from the technological point-of-view, video summarization is best classified into 5 broad categories: a) feature-based, b) clustering, c) event-based, d) shot detection, and e) trajectory-related.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Smart video summarization using mealy machine-based trajectory modelling for surveillance applications

Dogra

Ahmed

Bhaskar

2015

Multimed Tools Appl

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In this paper, we propose a smart video summarization technique that compiles a synopsis of event(s)-of-interest occurring within a segment of image frames in a video. The proposed solution space consists of extracting appropriate features that represent the dynamics of targets in surveillance environments using their motion trajectories combined with a finite state automaton model for analyzing state changes of such features to detect and localize event(s)-of-interest. We introduce the cumulative moving average (CMA) and the preceding segment average (PSA) statistical metric as features that indicate gradual and sudden changes in the instantaneous velocity of moving targets. In order to support both on-line and off-line summarization, a finite state machine, that is often referred to as Mealy Machine, has been proposed to model the trajectory of a moving target and used for detecting transitions that represents a change from one state to another when initiated by a triggering event or condition. We conduct several systematic experiments on different scenario-specific in-house videos and other publicly available datasets to demonstrate the effectiveness of our proposed approach and benchmark its performance against chosen baseline strategies. The results of our experiments highlight the superiority of our proposed Multimed Tools Appl method in accurately localizing the start and end of event(s)-of-interest in videos within the chosen dataset.

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Advances in Video Summarization and Skimming

Cited by 24 publications

References 45 publications

License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change

License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change

Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Smart video summarization using mealy machine-based trajectory modelling for surveillance applications

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