This paper gives a brief idea of the moving objects tracking and its application. In sport it is challenging to track and detect motion of players in video frames. Task represents optical flow analysis to do motion detection and particle filter to track players and taking consideration of regions with movement of players in sports video. Optical flow vector calculation gives motion of players in video frame. This paper presents improved Luacs Kanade algorithm explained for optical flow computation for large displacement and more accuracy in motion estimation.

1. Motion Trajectory of Players using Optical Flow
Ashwini D.Narhare1
, G.V.Molke2
and Dr. Rajendra Kanphade3
1
Department of E&TC,PG Student,Padmashree Dr.D.Y.Patil institute of engineering and technology,pune,India
Email: narhare.ashwini@rediffmail.com
2
Department of E&TC, Asso. Professor,Padmashree Dr.D.Y.Patil institute of engineering and technology,pune,India
Email: molkegv72@gmail.com
3
Principal,Nutan Maharastra institute of engineering and technology, Talegaon Dabhade, pune,India
Email:rdkanphade@gmail.com
Abstract— This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
Index Terms— optical flow analysis, improved Lucas Kanade algorithm, segmentation,
particle filter tracking
I. INTRODUCTION
Motion detection and tracking of players estimates motion of players. Tracking of players from video is
automatic method and it initializes player’s position and corrects mistakes by observing position during
tracking. Motion tracking of players is useful for broadcasters and sportsmen’s at individual level of player
so can improve quality of game. Coach can get information about team quality .Coach can give best
development in game. Also coach can get hide movements of players due to the eyes through the camera. In
broadcasting player’s motion information can enhance using graphical analysis and movement can display on
the screen to see for the viewers. Observation of movements of players is important to improve skill of player
and team energy. Also it is useful to understanding the game. Tracking motions of players provide picturing
of path in the game. Because of this the research is concentrating now a days to do analysis automatically or
semi automatically using different data source like statistical data of game or manually labeling events of
game .This task needs information of players movement . Motion trajectory data gives movement of players
which is important in motion analysis. For many years analysis of motion of players have done by
observation sheets. In 1980’ video recording is developed for motion understanding. Motion analysis of
players were done manually .It was time consuming and difficult task. Previous computer vision technology
was slow due to computational abilities. But recently tracking process is growing to understand motion of
players to do motion analysis of players. It is fast, time saving and easy technique to improve game for
players. Moving object detection in computer vision involves identification of the presence of an object in
consecutive frames where as it is used to monitor the movements with respect to the region of interest .Many
technology and methods developed for player’s motion trajectory.
Ref. [1], [2],[3],[5],[6], provided methods for tracking motion of players for motion analysis of games. Each
DOI: 02.ITC.2014.5.82
© Association of Computer Electronics and Electrical Engineers, 2014
Proc. of Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC

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author worked on the players movement information by using motion calculation algorithms .Players
tracking is done by using particle filters . Ref. [1], used motion estimation algorithm to know the motion of
players. Support vector machine recognized action of players. Tactics analysis of tennis is based on
recognized result and giving stroke is scored or not Ref. [2]represented action recognition on template based
methodology in basketball game did analysis of game in the form of small scale and large scale model
.Ref.[3], homography is computed by merging RANSAC with KLT and DLT (direct linear transform)
algorithm and tracked object with static background by using particle filter. Ref. [5] represented multiple
football players tracking using multiple particle filters. Segmented players region to know movement of
players.
Multiple states generated with filter follow the track of players. For tracking did players inter camera and
inner camera operations. Ref. [7], motion of players is estimated by using optical flow which is gradient
based assuming brightness is constant in neighboring pixel. Partial derivative of brightness is found and
applied edge detector. A motion characteristic within tracking area is done by statistical tool .Ref. [8], image
intensity function derivative solved and motion related statics generated.Ref. [9], algorithm is introduced
convergent optical flow computation and combined with Lucas Kanade pyramid different to LKP method
Implemented algorithm for tracking a single player in a sport.Ref.[10], presented flexible and expandable
approach for detection, tracking and recognition of players. For tracking of players particle filter is used. Ref.
[13], used particle filter for tracking and presented how particle filter has speed in calculation and accuracy.
This paper has six sections. Introduction part is over in section 1.Section 2 is about system overview and
problem definition. Section 3 covered input details. Sec. 4 explaining motion detection of players. Sec
5.providing Particle filter tracking of players. Sec. 6 is showing result.
II. PROPOSED SYSTEM
Now a day’s huge amount of multimedia information needs to develop motion analysis. These papers explain
motion detection and tracking method in the video sequence by using optical flow. The key to calculate
motion in video frame is optical flow. Optical flow is calculated by using improved lucas kanade algorithm.
Ref. [1],used horn-shunuk motion estimation algorithm. Ref.[2] did segmentation to get movement of players
Ref.[3], combination of KLT and DLT and RSNSC algorithm estimated motion of hockey players Previous
methods of motion estimation has limitation to calculate optical flow for big displacement of players and in
calculation accuracy. Present system gives more correctness in estimation and calculates large displacements
of pixel in image sequences.Fig.1, shows System for motion analysis of sports players.
Proposed sytem consists three parts.Video clip ,Detection of playres which consist of optical flow
analysis,multiscale lucas kanade pyramidal implememtation and segmentation,tracking of players using
particle filter.In the system it mainly has three parts one is video clipt,players motion detection and playres
tracking .video clip has number of frames.Frame extraction is done to get important and complete
imformation for motion detection and tracking of playres.Optical flow algorithm of Lucas Kanade applied
further for detection of players motion.Segmetation is done for thresholding which separates images to easily
read.Then tracking players is done by particle filter it updates the current state of players .It first finds current
state of player by using prediction equation then upade the state of players.
III. VIDEO CLIP
In the current information epoch, maximum data are representing and processing in multimedia form. In this
data video consists frames which contains similar information are usually processed. This cause to unwanted
time wastage, slow speed and results complexity. Video data has number of frames in sequence with different
frame rates. Frame rate also known as frame frequency or frames per second .Ref.[4], at this frequency (rate)
an imaging device produces unique consecutive images called frames.Frame are extracted from given input
video . It is essential to do analysis of video and select frames. In the huge video data sometimes invalid data
is transfer in the process. Frame extraction manages and passes valid video data in the system. For given
video this frames are use motion features. For given video this frames are use motion features. Video data
manages and processes due to extraction. For one second video, at least thirty frames are needed .Ref. [14, 4],
All frames are not required to display important information as frame is similar some of them don’t need.
Frame which need and has important information is key frame. When complete information of a video want
to display fast then key framed are needed instead of all frames. Key frames are then packed into file called
shot. Each key frame consists all necessary information in a video shot of the video. This is video

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summarization. It is also a compact illustration of a video sequence. Frame extraction is main feature of a
video summarization.
Figure 1.Motion estimation and tracking system using optical flow for sports players
IV. PLAYERS MOTION DETECTION
A. Optical Flow Analysis
Optical flow is a two-frame differential method for motion estimation. We use the Lucas–Kanade method
[Lucas and Kanade, 1981], which is one of the most popular & simple implementation. Only movement
between frames defines motion. Motion vector plays one important feature in moving object. However, the
motion vector is required to characterize the real motion displacement.
Optical flow is gradient-based approach, is estimated based on the rapid change in intensity of pixels. Optical
motion is pattern of apparent motion of objects and viewers in sequential frames. It calculates translation of
each pixel in a region. It is a field displacement vector or motion vector. Motion is calculated by motion
vectors using optical flow. It calculates distance of moving object from frame to frame using feature
extraction. Optical flow computation is easy and accurate methodology to identify moving object. It works
on assumption that brightness of neighbouring pixel is constant. Optical flow computes flows of motion from
adjacent frame. Fig. 2 is representation of motion of players in frame to frame. However, due to the optical
flow constraint, the obtained optical flow does not represent the true motion, but only the motion estimate on
the direction of image gradient.
Takes two images at time t and t+dt. Assuming image intensity at t and t+dt is constant. Then we can get
. (1)
Also assume small motion and after applying Taylor series getting
+ + H.O.T. (2)
. (3)
Rewriting equation
. (4)

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(4), equation which represent optical flow lying on x axis and optical flow component lying on y axis.
are the first derivative of image at (x, y, t).
Figure 2.Motion of players in sequence of frames
Simplifying and writing this equation and Put dx/dt=u and dy/dt=v by rewriting equation in terms of u and v
get,
. (5)
(u.v) is optical flow vector which is also motion vector.
Key assumptions of Lucas-Kanade Tracker 1) pixel’s neighbors have same (u, v).2) Brightness constancy:
projection of the same point looks the same in every frame. (x, y) are coordinates of frame t and (u, v) are
coordinates at t+1.u and v are image velocity .Taking small widow size w*w with w>1. (6) Creates one
equation and two unknowns. By applying equation to more pixels in the window which is centered at(x, y)
giving matrix form
(6)
Therefore AT
A =-AT
B where T
A
Now get =- (AT
A)-1
AT
B (7)
(5),is basic optical flow equation.In these system optical flow computation approach is improved Lucas-
Kanade algorithm for better accuracy called Multiscale Lucas kanade Pyramid. Lucas Kanade method is less
sensitive to noise developed by Bruce D. Lucas and Takeo Kanade. It is depend on assumption that optical
flow is constant in neighbouring pixels. It solves basic equations by the least square criteria to determine
optical flow. MSLKP assume input sequence with high resolution .At each pair of image in input sequence,
pyramid transform is applied to derive low resolution images. The result of one level on pyramid is correct
solution.
B. Improved Lucas Kanade Algorithm
Until now, were dealing with small motions. So it fails when there is large motion. So again we go for
pyramids. pyramid, removed small motions and large motions becomes small motions. So applying Lucas-
Kanade there, get optical flow along with the scale. At each level displacement of pixels calculated and
improved accuracy because of image sampling.
Fig. 3, is a multi-resolution representation of an image formed by few images, each one a subsampled version
of the original one at increasing standard aberration of frame at t and frame at t+1 .Lucas Kanade assume that
motion vector in any given region is constant it merely shifts form one position to another. Multiscale Lucas
Kanade Pyramid methodology is based on resolution of images. At each level of pyramid resolution of image
is changed. Image represented in Multiscales .Smoothing and subsampling is done of images .Firstly it reads

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image. And level L to L-1 variation in resolution of image is done. First level has original image. Ref.
[7][8], Taking Gaussian pyramid of images at time t and t+1 in the frame from high resolution to low
resolution and applying and running Lucas-kanade to each level from high level to lower level wrapping and
up sampling is done.
Steps in the algorithm:
• Optical flow is calculated in the deepest pyramid level using Lucas-Kanade flow algorithm.
• Guess displacement of pixels initially and result of lower level is applied to upper level
• At this level optical flow computation is done.
• The process repeated until highest level of pyramid.
Thus u and v is calculated. The result of optical flow computation is shown in Fig. 2, of players. By applying
Lucas Kanade on each level by up sampling and wrapping got a good result for large displacement of
players.For motion estimation optical flow gives motion vector μ and ν. Optical flow is motion vector where
it is corresponds to the movement of perceived feature.
Figure 3.Multiscale Lucas Kanade pyramid
C. Segmentation
Thresholding is approach for segmentation the images in video frames. Segmentation separates background
and foreground. To segment the movable object the motion vector determines the pixels in the frame is
moving or not. Thresholding is applied on optical flow to separate moving group of pixels. Thresholding
value is variable and its value varies from frame to the next frame. Factors like environmental conditions,
illumination and camera setting parameters changes value of thresholding. Handling small optical flow vector
is main challenge in segmentation .This difficulties for small optical flow vectors are due to background
noise.
The segmentation result is shown in fig.3
Where absolute value for optical flow and threshold value is mean absolute of the segmented object is
input to tracking .It is described using following condition,
Else f(x, y) =255
V. PLAYERS TRACKING
Tracking is done after segmentation. Ref. [1], [3], [5], [4], for Tracking of players particle filter is used
Particle filter track interested region and particles. Particle filters is also SMC sequential Monte Carlo
method. This algorithm estimate density by implementing Bayesian recursion equations. SMC method use
set of particles to estimate density. This methodology generates samples through state distribution.
Distribution samples are nothing but set of particles. Ref. [11], each particle consists of weight to represent
probability of that particle sampled from the probability distribution function It is robust over noise. In other
words, each particle is a guess representing one possible location of the object being which is to be tracked.
The set of particles contains more weight at locations where the object being tracked is more likely to be.

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Ref. [12],[13],this weighted distribution is propagated through time using Bayesian filtering equations, and
can calculate the trajectory of the tracked object by taking the particle with the highest weight. Particle filter-
based object trackers have proven to be very effective. Conceptually, a particle filter-based tracker maintains
a probability distribution over the state (location, scale, etc.) of the object being tracked.
Let rt-1 represent the state of tracked object at time t-1And ot-1 is observation at t-1.o1: t-1represent a set of all
observations up to t-1.All information about the target’s state rt-1is exemplified by its posterior P
(rt−1|o1:t−1).The aim of tracking is then to evaluate these subsequent new observations ot arrive. This
process is divided by two steps: 1. prediction 2. Update.
Prediction shown by using following equation
P (rt|o1: t-1) = ∫p (rt|rt-1) p (rt−1|o1:t−1)drt-1 (8)
Update step is shown by following equation
P (rt|o1: t)α p (ot|rt) p(rt|o1:t-1) (9)
Monte Carlo simulation is done for these steps. In application, we use condensation algorithm, means simple
particle filter, where the posterior at time-step t-1 is represented by a finite set of weighted particles.
Dynamical model describing the state progress P (rt|rt-1) and a model that estimates the likelihood of any
state given the observation P (ot|rt).Players’ tracking is shown in fig. 7.In which prediction of state of players
is estimated and then next state is updated. Observation is depending only on current state.
Particle filter tracking flow consist of following steps.
• Start with initial state and distribution.
• Draw samples to find current state means predict step.
• Using prediction equation find next state means update step.
• Defined the weight of particles.
• Resample the particles using resampling technique.
• Go to predict step till all observations are exhausted.
VI. RESULT
Covers here result and motion estimation and trajectory of player’s algorithm discussion. Algorithm tested
with real and various data under static and dynamic environment .Implementation and motion estimation
analysis is done and tested under the mat lab with its functionality. Simulation is done in Matlab to give
simulation of system. Fig. 4 simulation model in Matlab is shown for this system. Motion estimation is done
by calculating displacement of pixels from neighbouring frames. In the motion estimation system optical
flow algorithm is applied. Multiscale Lucas Kanade Pyramidal implementation is improved Lucas-Kanade
method. it gained better robustness and accuracy. Detection of player’s motion used corner feature extraction.
Figure 4.Motion estimation of player’s system simulation model under the Matlab using optical flow
Fig. 5,The optical flow vector result of players using Gaussian smoothing based Lucas Kanade with
pyramidal implementation under the Matlab.Pyramidal implementation of Lucas Kanade gives smoother
motion vector result with less density, high accuracy, and better speed in calculation of motion vector.

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Fig.6,Thresholding done segmentation of images it creates binary image. This gives simple representation of
images. It separate background and foreground images. It extracts foreground image. Some different
Figure 5.Motion vector of player’s using Multiscale Lucas Kanade Pyramid implementation in Matlab
problems need to be solving while tracking players 1.occulsion between players 2.size of players in
pixels3.players shape and colors. Particle filter is efficient for this solution in visual tracking. It handles
uncertainty and provides better result in complex background. It increases robustness and accuracy.
Figure 6. Result obtained by selecting threshold over motion vector result
Figure 7. Tracking of players using particle filter
VII. CONCLUSIONS
A system of trajectory of motion of players in any game is presented. Motion analysis of sports players is
now a demanding concept for study and understands improvement in game in which movement of players
estimated by using multi scale Lucas Kanade pyramidal implementation .MSLKP method is giving accurate
result for large displacement. Then players tracked on a pitch using a particle filter. This is robust and
effective tracking method for players.

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ACKNOWLEDGMENT
We place on record and warmly acknowledge the continuous encouragement invaluable support and timely
help offered by Dr. R. K. Jain, Principal, Pd.Dr.D.Y. Patil Institute of Engineering and Technology, Pimpri in
bringing this report to successful completion we express our sincere thanks to our teachers who have
rendered the basic knowledge and sowed the seeds of interest and inquisitiveness in each one of us. We
express our special thanks to all our friends who have patiently extended all sort of help directly or indirectly.
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