A Robust Method for Moving Object Detection Using Modified Statistical Mean Method

International Journal of Advanced Information Technology (IJAIT) Vol. 2, No.1, February 2012
DOI : 10.5121/ijait.2012.2106 65
A Robust Method for Moving Object Detection
Using Modified Statistical Mean Method
Safvan Vahora1
, Narendra Chauhan 2
, and Nilesh Prajapati 3
1
Dept. of Information Technology, Vishvakarma Government Engineering College,
Ahmedabad, India
2
Dept. of Information Technology, A. D. Patel Institute of Technology, Anand, India
3
Dept. of Information Technology, Birla Vishvakarma Mahavidyalaya, Anand, India
{safvan465 ,narendracchauhan}@gmail.com,
nilesh.prajapati@bvmengineering.ac.in
Abstract.
Moving object detection is low-level, important task for any visual surveillance system. One of the aim of
this paper is to, to describe various approaches of moving object detection such as background subtraction,
temporal difference, as well as pros and cons of these techniques. A statistical mean technique [10] has
been used to overcome the problem in previous techniques. Even statistical mean method also suffers with
the problem of superfluous effects of foreground objects. In this paper, the presented method tries to
overcome this effect as well as reduces the computational complexity up to some extent. In this paper, a
robust algorithm for automatic, noise detection and removal from moving objects in video sequences is
presented. The algorithm considers static camera parameters.
Keywords:
Moving object detection, Noise detection and removal, Statistical mean technique.
1 Introduction
Computer vision system have been developed, in order to simulate the most natural systems
which have ability to deal with changing environments such as moving objects, objects tracking,
changing illumination, and changing view point. In video surveillance system, detection and
tracking of object is lower level task that provides support to higher level tasks such as event
detection. Categorizing moving objects is a critical task which requires video segmentation,
which is used in number of computer vision applications such as video surveillance, traffic
monitoring, and remote sensing [1]. There are three major steps in video surveillance analysis:
detection of moving objects, tracking of interested objects from consecutive frames, and the third
is analysis of these tracked objects to identify its behavior, and also to identify normal/abnormal
events.
There are many applications those uses video surveillance. Video traffic monitoring is gathering
traffic information from various visual sources to redirect the traffic flow. Smart video
surveillance system is monitoring scene continuously, to detect a desired event. Gesture
recognition is to identify human gesture, fingerprint detection and eyes detection to login into a
system. Video indexing can be used for automatically explanation and retrieval of videos in
multimedia database.

66
Moving object detection process of classifying the pixels in video frames into the two classes,
foreground pixels are considered as pixels of moving object and background pixels are
considered as pixels of stationary background object. In another words, moving object detection
handles segmentation of from stationary background object.
Detection of moving object can be complex due to several reasons such as noise in video
sequence, sudden illumination changes, shadows and waving tree branches in the wind, rain or
snow-fall for outdoor video surveillance, occlusion provide loss of information by projection of
3D world in 2D image [9].
Different approaches have been proposed by different researchers for moving object detection. In
this paper, a modified statistical mean technique has been presented for moving object detection,
that having various advantages over the traditional approaches of moving object detection. The
presented method tries to reduce the effect of noise found during the detection of moving objects.
Hence it increases the robustness in detection process.
Figure 1 shows the framework of presented robust approach for moving object detection by noise
detection and removal. We describe each phase in subsequent section.
Figure 1: Framework of Noise Robust detection of moving object
The rest of the paper is organized as follows. In section 2, we give details of traditional
background subtraction technique, temporal difference and statistical mean technique of moving
object detection. In section 3, we describe a robust method for noise detection and removal during
the detection of moving objects. This has been achieved by using modification to statistical mean
technique which is the modified version of statistical mean technique as described in section 2.
Experimental results and conclusion are described in section 4 and section 5 respectively.
2 Methods for Moving Object Detection
2.1 Background Subtraction
Object detection [3, 6, 7, 8, 10] can be achieved by creating background model and then finding
deviations from the model for each and every frame in video. Background subtraction is widely
used because of its time complexity less for detecting the foreground object. Background
subtraction is particularly a for motion segmentation in static scenes [2].
Background subtraction is most popular choice to detect stationary foreground objects [6, 7],
because they work well when the camera is static and illumination change is gradual [8].
In this method, to detect the foreground object, the background subtraction algorithm detects the
difference between the current image and the background image or often called reference image
or background model. Recent background subtraction algorithms focused on robust background
modeling and updating to adapt to varying illumination condition between night and day, light
effects, background structures, background change from whether change and repetitive motion
from clutter [3]. For a pixel location (x, y) in the current image I, is identified as foreground pixel
along with background image B, if it satisfies the equation 1.

67
τ>− |),(),(| yxByxI (1)
whereτ is a predefined threshold [4]. The background image B is updated by the adaption
coefficient η as per the equation 2.
iii BIB )1(1 ηη −+=+ (2)
The core part of this method is to integrate the new upcoming information of a new image into
the background image, so that background image becomes more robust. The value of η should
not be large, it may create faster new changes in the background image.
The pixels constituting the regions undergoing change are marked for further processing. In
general, a connected component algorithm is used to obtain connected regions corresponding to
the objects.
Even though the background subtraction technique is well to extract moving object, this
background subtraction technique may suffer to dynamic background changes such as the
entrance of new background object or sudden illumination change.
2.2 Temporal Differencing
Temporal differencing technique uses the pixel wise difference between two or three frames in
frame sequences to extract a moving object. [5, 10] Temporal differencing technique is
computationally simple and fast as well as is adaptive to dynamic environment. In temporal
difference technique, extraction of moving pixel is simple and fast. It takes difference of the
current and previous frames as shown in equation 3
τ>− − |),(),(| 1 yxIyxI ii (3)
whereτ is predefined threshold.
Temporal differencing technique is less effective in extracting all information about target object,
especially when the target object has uniform texture or target object moves slowly. When a foreground
object moves slowly or stops moving in between, temporal difference technique fails in detection a change
between consecutive frames and loses the target object information. Temporal difference is most sensitive
to the threshold valueτ , to determining pixel-wise difference between consecutive frames as well as it may
left holes in foreground objects. Temporal difference require special supportive algorithm to detect stopped
objects.
2.3 Statistical Mean Technique
There are some drawbacks of traditional moving object detection approach, as described in the
above section. To overcome these shortcomings, we propose the next technique is statistical mean
technique. Statistical mean technique [10] is based on the computed statistics of each and every
frame of video or computed statistics of every k frames by using down sampling to reduce the
processing time. In statistical mean technique base, background image is calculated by computing
mean of each and every pixel available in video. For n frames and (x, y) is the pixel position of Ith
frame, the summation of all frames computed as shown equation 4

68
∑=
=
n
i
yxIyxX
1
),(),(
(4)
The summation of each pixel is in X (x, y) of n frames normalized mean model frame N(x, y), is
calculated as by dividing each pixel position with n, number of frames as shown in equation 5
nyxXyxN ),(),( = (5)
Compare this mean model N(x, y) to each and every frame from 1 to n. For every pixel (x, y) of
frame I, compare that pixel position with pixel position (x, y) of normalized mean model frame N
as shown in equation 6
τ>− |),(),(| yxIyxN (6)
where τ is a predefined threshold value. If the current pixel (x, y) of frame I, fall in this category
than specify it as foreground object pixel otherwise specify as background pixel.
Although statistical mean technique gives good result, but something it suffers by the problem of
superfluous effects of foreground objects. Whenever, object or more than one objects remains in
video for long period of time, for example, an object is presents in half of the number of frames
out of total n number of frames. This leads to erroneous mean model, so that the precise result of
moving object detection will not be achieved. This erroneous result is created because of the
superfluous effect of that object. To overcome the problem of superfluous effect of the object,
modified statistical mean technique is suggested as shown in the following section.
3 Modified Statistical Mean Method with Noise Removal
In this section, we present modification to statistical mean method discussed in the previous
section. The steps for the modified statistical mean method with noise removal are as follows:
1. Find out incremental statistical mean model initially
2. For each kth
(initially k=1) video frame
3. Compare incremental statistical mean model with kth
frame
4. Differentiate Foreground and Background Pixels
5. Low pass filters are used as “Pixel Level Process” for reducing blurring effect as pixel
level noise reduction.
6. The morphological operations are applied as “Detection of Connected Regions” to find out
connected region of the objects.
7. In “Region Level Process”, the erroneous object segmentation is removed by applying
threshold on to the properties of the region.
8. Extract all the available regions as Moving objects
9. Update incremental statistical mean model and repeat from step 2
3.1 Modified Statistical Mean Method
In modified statistical mean technique which we also call incremental statistical mean technique,
initially take k number of frames out of total n frames to calculate mean model N. This mean
model N is used for detection of moving object for first frame. As the first frame is processing,
simultaneously update mean model N, by taking next frame in derivation of mean model as
shown in equation 7. Here, next frame is I(k+1) to update the mean model.
1)),(*),((),( 1 ++= + kyxIkyxNyxN koldnew (7)

This updated mean model Nnew
detection. This process is repeated until it reaches to n, total number of frames.
3.2 Pixel level Processing
The methods discussed above are generally not sufficient to get desired moving object detection
output. Generally, it affects by various noise factors. To overcome this dilemma of noise, it
requires further pixel level processing.
We use low-pass filter for the pixel map that leads to
implementation of these operations is the removal of noise pixels in the foreground that does not
correspond to regions leading real and remove noisy pixels nea
covered by the pixels which is first plane background.
A Gaussian filter smoothing an image by calculating a weighted average filter coefficient
Gaussian filter modifies the input signal by convolution with a Gaussian
dimensions, Gaussian filter is the product of two such Gaussians, one per direction as described
in equation 8
(g
where, x is the distance from the origin in the horizontal axis, y is the distance from the
the vertical axis standard deviation of the Gaussian distribution.
response of the 2D Gaussian filter
Figure 2:
Our aim in applying these operations is removing noisy
to actual foreground regions, and to remove the noisy background pixels near and inside object
regions that are actually foreground pixels.
3.3 Finding Connected Regions
In this phase the filtered foreground pixels
erosion dilation morphological operations are applied
Advanced Information Technology (IJAIT) Vol. 2, No.1, February 2012
new is used to for further processing to find the moving object
detection. This process is repeated until it reaches to n, total number of frames.
requires further pixel level processing.
pass filter for the pixel map that leads to eliminate noise[11].Our objective in the
correspond to regions leading real and remove noisy pixels near the bottom and in the regions
covered by the pixels which is first plane background.
A Gaussian filter smoothing an image by calculating a weighted average filter coefficient
Gaussian filter modifies the input signal by convolution with a Gaussian function. In two
( ) e
yx
yx 






 +
−
∏
= 2
2
2
2
2
1
, σ
σ
(8)
where, x is the distance from the origin in the horizontal axis, y is the distance from the
the vertical axis standard deviation of the Gaussian distribution. Fig. 2 shows the frequency
response of the 2D Gaussian filter
Frequency response of the 2D Gaussian filter
Our aim in applying these operations is removing noisy foreground pixels that do not correspond
regions that are actually foreground pixels.
Connected Regions
he filtered foreground pixels are grouped into connected regions. The subsequent
morphological operations are applied to find out connected region of the objects.
Advanced Information Technology (IJAIT) Vol. 2, No.1, February 2012
69
is used to for further processing to find the moving object
11].Our objective in the
r the bottom and in the regions
A Gaussian filter smoothing an image by calculating a weighted average filter coefficient[12].
function. In two
where, x is the distance from the origin in the horizontal axis, y is the distance from the origin in
shows the frequency
foreground pixels that do not correspond
The subsequent
to find out connected region of the objects.

70
3.4 Region Level Process
As the elimination of noise at the pixel level, a few small areas are artificial because of bad
segmentation. This erroneous object segmentation are removed by applying threshold on to the
properties of the region.
4 Experimental Results
We tested our method incremental statistical mean method on various sequences of videos
acquired on outdoor environments. Figure 3 shows the output of the temporal difference method,
by applying the operation as described above, under the outdoor environment. Figure 3 (a) shows
the frame sequence 106 to 109 and figure 3 (b) shows frame sequence 107 to 109 of the detected
object after applying temporal difference technique as described, in section 2. For example,
shown in figure 1, the value of threshold τ is 30.
(a)
(b)
Figure 3: Temporal Difference for moving object detection, (a) input video frame sequence 106 to 109, (b)
Temporal Difference result sequence 107 to 109
As described in section 2, it may leave holes in foreground object detection as shown in result. As
stated it requires the support of another algorithm to get accurate output.

71
Figure 4 shows, the output by applying the incremental statistical method to the video sequence
acquired outdoor environment. It provide, detection of moving object accurate, it is able to detect
moving object under the dynamic change in the background stationary model. For the example
shown in figure 4, the value of threshold τ is 30.
(a)
(b)
Figure 4: Incremental statistical mean Technique, (a) Input video frame sequence 101 111 121 131 (c)
Moving object detected sequence 101 111 121 131
Figure 4 (a) input frame sequence 101 111 121 131 of the video, and 4 (b) shows the output frame
sequence 101 111 121 131 that contains moving object detected using the incremental statistical
mean method, by using the equation specified in the above section.
Figure 5 shows the results after removing the noise from the video frames. Figure (a) shows
input video frames of video 1 and video 2, (b) shows moving object without applying the noise
removal methodology and (c) shows the effective result moving object after removing the noise.
We have applied this technique on 12 video, indoor as well as the outdoor environment, and gives
accurate result for those. Though, incremental statistical mean technique, achieves good result for
moving object detection, it have some disadvantages as, it cannot deal with sudden rain or snow
fall, in the video. It cannot differentiate the rain drops and moving object from the video frames.
This technique

72
(a)
(b)
(c)
Figure 5: Noise Removal Results (a) Input video frames of video 1 and video 2 (b) Output of object
detection without removing noise (c) Output of object detection after removing noise
requires parallel processing to reduce the time complexity. If the value of threshold is not chosen
appropriate, to the environment, it may produce the inaccurate result.

73
5 CONCLUSION
Moving object detection is the first and low-level step of any visual surveillance system. The
traditional moving object detection techniques, with its pros and cons have been explained in
brief. These drawbacks are also described experimental results. We have presented a modified
statistical mean technique with noise removing process that is computationally fast, by allowing
the parallel processing. Modified statistical mean technique, overcome the drawbacks of
traditional approach of moving object detection. The noise remove process removes the noise
caused due to camera, reflectance noise and gives effective moving objects, as well as provides
effective results in the change of light during the day and night, the model of background is
changed.
References
1. Elhabian S. Y., El-Sayed K. M.: Moving object detection in spatial domain using background
removal techniques- state of the art, Recent patents on computer science, Vol 1, pp 32-54, Apr, 2008.
2. McIvor A. M.: Background subtraction techniques. Proc. of Image and Vision Computing, 2000.
3. In Su Kim, Hong Seok Choi, Kwang Moo Yi, Jin Young Choi, and Seong G. Kong. : Intelligent
Visual Surveillance - A Survey. International Journal of Control, Automation, and Systems (2010)
8(5):926-939
4. Heikkila J. and Silven O.: A real-time system for monitoring of cyclists and pedestrians. Proc. of
Second IEEE Workshop on Visual Surveillance, pages 74–81, JUNE 1999.
5. Lipton A. J., Fujiyoshi H., and Patil R. S, : Moving target classification and tracking from real-time
video, Proc. of the IEEE Workshop Applications of Computer Vision, pp. 8-14, 1998.
6. Mathew R., Yu Z., Zhang J. : Detecting new stable objects in surveillance video, Proc. of MSP 2005,
pp. 1-4
7. Liao H.H., Chang J.Y., Chen L.G. : A localized Approach to abandoned luggage detection with
Foreground –Mask sampling, Proc. of AVSS 2008, pp. 132-139.
8. Bayona Alvaro, SanMiguel Juan C., Martínez Jose M. : Stationary Foreground Detection using
Background Subtraction and Temporal Difference in Video Surveillance. Proceedings of 2010 IEEE
17th International Conference on Image Processing September 26-29, 2010.
9. Yilmaz, A., Javed, O., and Shah, M. 2006.: Object Tracking: A survey. ACM Comput. Surv. 38, 4,
Article 13, Dec 2006.
10. Kotikalapudi U. K. Abnormal event detection in video, M.Tech Thesis, Indian Institute of Science,
Banglore, July 2007.
11. Gonzalez R. C. and Woods R. E., Digital Image Processing (third edition), Reading,
Massachusetts:Addison-Wesley, 1992
12. Sapiro G. and Ringach D. L. , “Anisotropic diffusion of color images”, In Proc. SPIE,
1996,2657:471–382.

A Robust Method for Moving Object Detection Using Modified Statistical Mean Method

More Related Content

What's hot (20)

Similar to A Robust Method for Moving Object Detection Using Modified Statistical Mean Method (20)

Recently uploaded (20)

A Robust Method for Moving Object Detection Using Modified Statistical Mean Method