An Efficient Method For Gradual Transition Detection In Presence Of Camera Motion

International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 6, June 2014. ISSN 2348 - 4853
16 | © 2014, IJAFRC All Rights Reserved www.ijafrc.org
An Efficient Method For Gradual Transition Detection In
Presence Of Camera Motion
Salim A. Chavan1, Amol A. Alkari2, Dr. Sudhir G. Akojwar3
1Department of E&TC, Associate Prof and Vice Principal, DBNCOET, Yavatmal.
2Department of E&TC, PG Student, DBNCOET, Yavatmal.
3Department of E&TC, Professor and Head, RGCER&T, Chandrapur.
salimsahil97@rediffmail.com1,amolalkari@gmail.com2,sudhirakojwar@rediffmail.com3
A B S T R A C T
Gradual transition detection is one of the most important issues in the field of video indexing and
retrieval. Among the various types of gradual transitions, the fade and dissolve type the gradual
transition is considered the most common one, but it is most difficult one to detect. In most of the
existing fade and dissolve detection algorithms, the false detection problem caused by motion is
very serious. In this paper we present a novel gradual transition detection algorithm using local
key-point integrated with twin comparison method that can correctly distinguish fades and
dissolve from object and camera motion.
Index Terms: Gradual transition Detection, Fades, Dissolve, Recall, Precision and Retrieval
Success Index.
I. INTRODUCTION
The necessity for intelligent processing and analysis of multimedia information has been rising on a
regular basis. Researchers have built a number of technologies for intelligent video management which
includes the shot transition detection, key frame extraction, video summarization, video retrieval and
more. Gradual transition detection is considered to be the most difficult and significant issue of practical
value amongst all the others.
Videos have become a popular means of entertainment over the years. Traditionally, videos were created
only by a limited number of producers. But now, the commoners as well can afford and use with
simplicity the video capturing devices, as a result of which there is an increase in the amount of user
generated videos. A large collection of videos is readily available on various video sharing websites.
Searching for videos with desired content from such a large collection has become a tedious task. Also,
viewers want to have a better control over the video data. As a result, many video browsing, indexing and
summarization applications are being developed.
Altogether the amount of video data available to anyone increases more rapidly than anybody can handle
on his or her own. Computers and video search engines are needed to make it possible to find and access
relevant information from this huge amount of data. Most of the video search engines available to the
public are still based on textual search and are thus dependent on manual annotation of the data.
However, manual annotation is slow, expensive and sometimes inaccurate and heterogeneous since the
annotations are always subjective and dependent on the annotator’s cultural background, language and
opinions. Automatic annotation methods are needed to really be able to access all the video data
available. It assists the users in the retrieval of favoured video segments from a vast video database
efficiently based on the video contents with the aid of user interactions. In general, the video retrieval
system can be divided into two principle constituents i.e. a module for the extraction of representative
characteristics from video segments and defining a fitting similarity model to position similar video clips

from video database. A large number of approaches employed a wide variety of features to symbolize a
video sequence. One of such approach is local key point approach which is stated in this paper.
A common first step for shot boundary detection is to segment a video into elementary shots, each
comprising a continuous in time and space. These elementary shots are composed to form a video
sequence during video sorting or editing with either cut transitions or gradual transitions of visual effects
such as fades, dissolves and wipes. Shot boundaries are typically found by computing an image-based
distance between adjacent frames of the video and noting when this distance exceeds a certain threshold.
The distance between adjacent frames can be based on statistical properties of pixels, compression
algorithms or edge differences.
There are two basic types of shot transitions abrupt transition and Gradual transition. Gradual shot
change detection is one of the most important issue in the field of video Processing. The study shows that
it is comparatively easy to detect hard transition but detection of gradual transition is challenging issue.
In this paper key points in the frames are detected and then key point of adjacent frames are matched to
detect the gradual transition. This works efficiently for scaling and rotation of frames. This approach
gives an efficient algorithm for abrupt and gradual transition in presence camera as well as objects
motion.
II. RELATED WORK
An effective algorithm for dissolve detection with camera and objet motion is proposed in [1]. This paper
provides an approach to overcome the problem of misdetection and false detection by modeling a
dissolve transition and selecting a proper threshold. Although there are problem in Illumination this
algorithm works with high efficiency. An approach to detect the gradual transition and its type by using
B-spline Interpolation is explained in [2]. Special detectors are used to detect the type of transition. The
problem of detection of fades in film cadence is mentioned in [3]. The difference in video and film
cadence and an approach to detect fades in film cadence is explained in above paper. The method to
detect fades by observing changes occurring in histogram is explained in [4]. There is an increased need
to extract key information automatically from video for the purposes of indexing, fast retrieval, and scene
analysis. To support this vision, reliable scene change detection algorithms is explained in [4]. The
algorithms have been proposed for both sudden and gradual scene change detection in uncompressed
and compressed video. The author used the properties of the fading operation and extracts these features
in the luminance histogram. In this an image is divided into four parts and histograms of all the four parts
are considered to detect the fade type. Results show that the proposed algorithm can be used in both
uncompressed and compressed video to detect fade regions with a high reliability. The approach to
detect dissolve based on accumulating histogram difference (AHD) with the support point is explained in
[5].
Two different algorithms for detection of dissolve and wipe based on image histogram are proposed in
[6]. A fuzzy logic approach to integrate hybrid features for detecting shot boundaries is explained in [7].
In this paper, author proposes a fuzzy logic approach to integrate hybrid features for detecting shot
boundaries inside general videos. The fuzzy logic approach contains two short dissolved shots, and the
other for detection of gradual shot cuts. These two modes are unified by processing modes, where one is
dedicated to detection of abrupt shot cuts including that mode-selector to decide which mode the scheme
should work on in order to achieve the best possible detection. The hybrid features used in this paper are
color histogram, texture change and edge variance are integrated for better performance in this paper.
The partition of video into shots and detection of abrupt and gradual transition using foveated
representation of video is explained in [8]. Foveated imaging is the technique in which the image has
different resolution at different parts. A method to partition a video into shot is proposed in [9] where

the motion of image at each time instant is represented by two dimensional models. An adaptive time
window is applied for shot boundary detection is explained in [10]. The local color information is used to
eliminate false detection by abrupt change of illumination such as camera flash and thunder.
Detection of gradual transition i.e. fade in, fade out, and dissolves by selecting a threshold by considering
an adaptive, robust and analytical study of mathematical model of transition is proposed in [11]. Here the
objective has been to accurately classify the type of transitions (fade-in, fade-out and dissolve) and to
precisely locate the boundary of the transitions. The false detection is removed by motion transition
removal. A neural network algorithm to detect the dissolve is explained in [12] where by giving large no
dissolve as an example are taken to create a dissolve synthesizer. The examples used to create the
synthesizer are taken from video database with high accuracy. These all examples are provided as a
training set to the neural network for dissolve detection. The approach to detect shot boundary by using
edge complexity is explained in [13]. The image quality of archived films often degrades gradually which
makes false detection during shot segmentation. The problem about the scene change detection in
archived films is explained in [14]. For abrupt change shot detection, the difference between histograms
of block-bases adjacent frames and across frames based on the brightness information is used, the similar
histogram problem is treated and the error caused by flicker is eliminated. Then the gradual change of
shots is detected based on the fact that the variances of inter-frame are usually monotonically increasing
in fade-in sequence and monotonically decreasing in fade-out sequence. The image quality of archived
films often degrades gradually so to detect scene change in such scenario an effective method is proposed
in [14]. A fuzzy logic approach to detect the shots in sport video is explained in [15].The fuzzy logic
approach is consider for two reasons there is no fix or hard limit for threshold selection as the hard
threshold makes the decision binary and there is no need of large training set. In fuzzy logic approach the
problem of shot boundary detection is formulated in terms of features used to detect the shot boundary
with certain value of membership function. In [16] author has proposed a new method for shot change
detection that is less sensitive to object or camera motion due to the robustness of the feature tracking
algorithm. A method for finding type of transitions is also proposed. The detection problem is solved by
using object recognition techniques, rather than some overall features, so that shot changes can be
distinguished from objector background motions in a scene. The advantage in [16] is that the method
does not completely depend on threshold of the number of matched points; the threshold applied in our
method is varied with the Local maxima and minima of the number of matches which can handle the
variations of transitions better. Also the method do not match neighboring frames or frames apart from a
fixed period only, but also match nonadjacent frames inferred by shot-change interval estimation which
can further increase the detection accuracy.
III. METHODOLOGY
The methodology consist of two main parts first one is finding key point and defining CCH descriptor and
second one is using twin comparison approach. The important steps involved in gradual transition
detection method these are described below
A. Defining a CCH Descriptor
In this method instead of considering all feature, high level features are consider. These high feature
points are extracted from an image by considering its RGB values. The main issue in developing invariant
local descriptors is how to represent a region more effectively and discriminatively. The color histogram
is one option for textural explanation; but it is sensitive to illumination changes. Instead, we consider a
technique that computes the contrast values of points within a region with respect to a salient corner
(Key points). We assume that many key points have already been extracted from an image. For each key

point Pc at the image coordinate (Uc, Vc), we locate an n*n local region R surrounding Pc. Let P denote a
pixel at the image coordinate (u, v) in region R. We compute the contrast value of C (P) of P in R as
C(P) = I (P) – I(Pc)
Where I (P) and I (Pc) are the intensity values of P and Pc respectively. We then construct a descriptor of
Pc based on these contrast values and separate R into several non-overlapping regions R1, R2…..Rt.
Without loss of generality, we use a log-polar coordinate system (r, ө) to perform the division, as shown
in Fig.1.
Figure 1. Log Polar Diagram of CCH descriptor
To ensure that the descriptor is invariant to image rotations, the direction of in the log-polar coordinate
system is set to coincide with the edge orientation of Pc. Considering the importance of representing a
sub region Ri efficiently and discriminatively, we consider a histogram-based representation because a
histogram is relatively insensitive to non-uniform deformations of a region. A perceptive way to employ
the histogram feature is to gather the contrast values in a sub region into a histogram bin. However,
summations of positive and negative contrast values may reduce the discriminating response of the bin.
Thus to improve the discriminative ability of the descriptor, we use both positive and negative histogram
bins of contrast values for each sub region, as described below For the sub region Ri , we define the
positive contrast histogram bin respective to Pc as
Where Ri+ is the number of positive contrast values in R . In a similar manner, the negative contrast
histogram bin is defined as
Where Ri- is the number of negative contrast values in R.
By combining the contrast histograms of all the sub regions Pc into a single vector, the CCH descriptor of
in association with its local region R can be defined as follows
CCH (Pc) = (HR1+, HR1-, ................ HRi+, HRi-)

This CCH descriptor was evaluated by using a large set of images undergoing various geometric
transformations. The evaluation results show that the CCH descriptor is efficient and highly accurate in
determining feature correspondence.
B. Locating Transitions by Matching Adjacent Frames
At this stage a set of local descriptor and key point is ready. We observe that objects or scenes are
replaced during transitions, even though they may be moving or rotating within the shot. Most methods
of shot change detection produce many false alarms when objects or cameras move, as they can only
detect changes in some overall features between the same image locations of adjacent frames in a video.
Although such features will change dramatically during transitions, they will also change when
something moves in a single shot. The advantage of feature matching is that it is invariant to
transformations; thus, we can even match objects after they have moved. An additional advantage is that
we do not have to design a detector for each kind of transition. Since a shot change indicates a change of
objects in the scene, multiple kinds of shot changes can be detected in a unified manner.
Figure 2. Feature matching results between adjacent frames.
In our algorithm, each frame is pre-processed by key point detectors. The key points are extracted as
mentioned above. A salient key point is selected by detecting the local maxima in an n*n region. Fig.1
illustrates the contrast context histogram of a salient key point under the log-polar coordinate system. A
local region is divided into several sub regions by quantizing and plotted on log-polar coordinate system.
For each sub region, a 2-bin contrast histogram, introduced above is constructed. Each key point in first
frame is matched to the key point in the next frame that has the shortest distance to it. However, if the
shortest distance is longer than a predefined minimum threshold, the key point is not matched to any key
point then it is declared as non-gradual or abrupt transition. While calculating the gradual transition, the
minimum matching between adjacent frame is consider and this threshold will definitely less than the
threshold that we have considered in case of abrupt transition. Thus we have defined two threshold in
our method upper and lower from which we separated abrupt and gradual transition.
C. Intervals of Transitions
When we determine that in this particular video, the gradual transition occurs, it is also necessary to find
the intervals of transitions. Our method for finding the intervals is also based on feature matching. Shot
changes are likely to occur when the number of matched objects decreases, thus there should not be any
transitions when several objects in adjacent frames are matched. In our method, the local maxima to the
left and right of the candidate transition are possible start and end frames of that transition. We add
another condition that the video sequence before and after the shot change should also be stable
resulting in stable numbers of matched key points. This find for start and end points begins with the two
maxima and continues until the number of matched key points is stable.

D. The Twin Comparison Approach
In this paper, the twin comparison approach was implemented to detect shot boundaries. Zhang was the
first researcher who proposed this method. In the twin comparison method, two successive frames are
computed according to their histogram differences metric. The next step is to pick out two thresholds;
high and low, which will be used on the obtained result. More precisely, cut transitions are detected if the
difference is greater than the high threshold TH. Gradual transitions are detected if the differences are
greater than the low threshold TL. The differences are accumulated until it becomes lower than the low
threshold TL. Finally, the high threshold is compared with the obtained accumulated difference in order
to detect gradual shot boundary. In general, the twin comparison approach is applied to several metrics
to detect gradual transitions. Here, the CCH histogram comparison difference metric is implemented to
detect the gradual changes in video.
IV. PERFORMANCE MEASUREMENT AND MANUAL DATA SET
A. Performance measurement
Recall and Precision are the two best and universally used performance matrices for analyzing any shot
boundary detection techniques. For demonstrating the performance of our method, both recall and
precision are computed. The recall and precision are defined in the following equations.
Recall =
Correct
* 100
Correct + Missed
Precision =
Correct
*100
Correct + False positive
Where the ‘Correct’ refers to the number of correct and the ‘Missed’ refers to the number of missed
detections while ‘False Positive’ denotes the number of false detections or false positive detections. If the
numbers of missed and false positive transitions are low, the performance of this algorithm can be more
accurate. On the other hand, ‘Correct + missed’ stands for the overall number of real transition in the
video under testing while ‘Correct+ False Positive identified the overall number of transitions detected by
this algorithm.
Recall tells us which proportions of the detected transitions were truly shot boundaries and Precision
describes in what proportion the algorithm is giving false detection.
Usually both recall and precision are given to fully describe the performance of a system since the values
are somewhat interlinked. Usually the system parameters can be altered to gain high precision values,
but this decreases recall. On the other hand, the parameters can be tweaked to gain high recall values, but
this often decreases precision. The goal is to find a compromise that gives sufficiently good precision and
recall at the same time.
There exist various ways to combine the precision and recall values to one single performance score. One
of these ways is the F1 measure which is a harmonic mean value that gives equal weight to both precision
and recall.

F1 measure = 2 *
Recall. Precision
Recall + Precision
In the evaluation, precision and recall values were calculated for gradual transitions. In addition, total
precision and recall were calculated by combining the two detection results. One more performance
measure to check the accuracy of the algorithm is Retrieval Success Index (RSI) given by the following
equation.
RSI =
Correct
*100
Correct +Missed +False Positive
B. Manual Data Set
The proposed algorithm was applied and tested on a data set of approximately one hour and forty eight
minutes of multiplicity videos. The chosen data set consist of six types of videos. The frames have been
extracted from data set video by using free studio tool generating 146452frames as a total length. All
video frames have been stored as JPG format involving about 4 GB of space. Furthermore, the fades and
dissolve transitions have been determined with a human observer. Table 1 exhibits the transitions
among the video list having 2005fades and 936dissolve transitions. The six types of selected video were:
Video 1: The video 1 is remix song of Ashiqui 2 movie with high amount of light effect. It contains total
7425frames with a frame rate of 25 frames per second and lasting for four minutes fifty seven second.
Video 2: The video 2 is Jeene laga hoon song from Ramaiya Vastavaiya movie contains total 1905frames
with a frame rate of 15 frames per second and lasting for two minutes seven seconds.
Video 3: The video 3 is Dil Tu Hi Bata song from Krish 3 movie with complete camera and object motion.
It contains total 4475 frames with a frame rate of 25 frames per second and lasting for two minutes fifty
nine seconds.
Video 4: The video is I am in love Song from Once upon a time in Mumbai movie. It contains total
4825frames with a frame rate of25 frames per second and lasting for three minutes thirteen seconds.
Video 5: The video 5 is Jumper movie. It contains total122110frames with a frame rate of 29 frames per
second and lasting for one hour thirty minutes.
Video 6: The video 6 is a Tu Jane Na Song from Ajab Prem ki Gajab Kahani movie.It contains total
5712frames with a frame rate of 24 frames per second and lasting for three minutes fifty eight seconds.
Table 1. The Manual Data Set for Gradual transitions.
Video Clips No. of frames Fades Dissolve
Video 1 7425 521 167
Video 2 1905 95 15
Video 3 4475 116 192
Video 4 4825 747 303

Video 5 122110 412 92
Video 6 5712 114 167
Total 146452 2005 936
V. RESULTS AND ANALYSIS
To measure the accuracy of detecting the change between shots a comparison has-been done between
the shot boundary that detected manually and those that detected using the twin comparison algorithms.
The Correct, False & Missed detected transition given by the algorithm is presented in Table 2. The
results obtained for various performance metrics are presented in Table 3 and Table 4 below.
Table 2 Results of Correct, Missed & False detected transition.
Video
Collection
Fades Dissolve
Correct
Detection
Missed
Detection
False
Detection
Correct
Detection
Missed
Detection
False
Detection
Video 1 500 21 46 160 7 20
Video 2 83 12 11 12 3 3
Video 3 91 25 0 173 19 10
Video 4 688 59 75 231 72 30
Video 5 383 29 32 78 14 11
Video 6 107 7 11 146 21 18
Table 3 Results in terms of Performance Matrices
Video
Collections
Fades Dissolve
Recall Precision Recall Precision
Video 1 95.97 93.63 95.80 92.21
Video 2 87.36 88.29 80 80
Video 3 78.44 100 90.10 94.53
Video 4 92.10 90.17 76.23 88.50
Video 5 92.96 92.28 84.78 87.64
Video 6 93.85 90.67 87.42 89.02
Table 4. Results of F1 measure and RSI
Video
Collections
Fades Dissolve
F1Measure RSI F1measure RSI
Video 1 94.78 88.18 92.21 85.56
Video 2 87.82 78.30 80.00 66.66
Video 3 87.91 78.44 92.26 85.64
Video 4 91.12 83.69 81.90 69.36
Video 5 92.61 86.26 86.18 75.72
Video 6 92.23 85.6 88.21 78.92
We have used the local key point approach integrated with twin comparison method which mainly
focused on constructing of histogram based on relative intensities instead of taking direct intensities. The

obtained results are tabulated and we get average 90% and 92% value of recall and precision
respectively. 60% of the above videos are such that it contains large amount of camera motion or light
effect. The f1measure and RSI obtained by our method have also very high value as compared to the
previous approaches. The trade-off between Recall and Precision is also very proper. Following graph
shows trade-off between recall and precision for Fades and Dissolve detection
Figure 3 Trade-off between Recall and Precision for Fades
Figure 4 Trade-off between Recall and Precision for Dissolve
VI. CONCLUSION
The paper mainly focused on detection of gradual transition in different video sequence. Due to the large
amount of capturing, storing and processing devices, it is almost impractical to analyze the video
manually.
In this paper we used a local key point approach to detect gradual transition. In this method, key points
are extracted, depending on that local descriptor is built and positive and negative histogram of relative
intensities is plotted which is used for the detection of gradual transition. For the detection of dissolve
twin comparison method is integrated with local key point approach.
Total 146452 frames are observed out of which around 16 thousand frames contains camera motion. The
average value of recall and precision is found to be 90% and 92% respectively. The maximum value of
f1measure and RSI are 93.49% and 86.87% respectively which is relatively very high as compared to
previous methods.

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An Efficient Method For Gradual Transition Detection In Presence Of Camera Motion

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An Efficient Method For Gradual Transition Detection In Presence Of Camera Motion