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Moving Object Detection Algorithms

1. Introduction

Detecting regions that correspond to moving objects in a video sequence plays a veryimportant role in many computer vision applications. Object detection from video

sequence is the process of detecting the moving objects in the frame sequence using

digital image processing techniques.

Moving object detection is the basis of moving object identification and tracking.

Although a lot of studies have been conducted in recent years the subject is still

challenging.

Moving object detection becomes challenging due to several reasons such as

Loss of information caused by the 3D world on a

2D image

Noise in images

Complex object motion

Non-rigid or articulated nature of objects

Partial or full object occlusions

Complex object shapes

Scene illumination changes

Dept of C S & E, B.I.E.T.,Davanagere Page 1


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2. Difficulties in moving object detection and tracking

Tracking of detected moving object in an image sequence is a significant and difficulttask. It is a crucial part of smart surveillance system, since without tracking; the system

could not extract cohesive temporal information about objects and higher level behavior

analysis steps could not be possible. On the other hand, due to occlusions and reflections

tracking becomes a difficult research problem.

Most tracking systems often fail under some situations. This could be either because of

illumination changes, pose variations or occlusions. Therefore the need for automatic

performance evaluation emerges in these applications. Short and long term dynamic

scene changes such as repetitive motions (e.g. waiving tree leaves), light reflectance,

shadows, camera noise and sudden illumination variations make reliable and fast moving

object detection difficult.

Hence, it is important to pay necessary attention to object detection step to have reliable,

robust and fast visual surveillance system.



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3. Introduction to Normalized Cross Correlation (NCC)

Algorithm

Normalized cross correlation (NCC) algorithm is based on finding the cross correlation

between two consecutive frames in an image sequence. Correlation is basically used to

find the similarity between two frames. If the two consecutive frames are exactly same,

then the value of Normalized cross correlation is maximum. In that case no moving

object is detected.

Now suppose there is a moving object in the image sequence, means the two consecutiveframes are no exactly same, with respect to positions of the pixel values. In that case the

value of Normalized cross correlation is less than maximum value obtained.

This concept of Normalized cross correlation is used for the detection of moving object in

an image sequence.



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4. Theory of NCC (Normalized Cross Correlation)

Correlation is mainly used for measuring similarity between two images. It is useful infeature recognition and registration. Normalized cross correlation is given by equation

(1).

In this, A and B indicates average pixel value in image A and B respectively. r is

normalized with respect to both the images and it always lies in the range [-1, 1].



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5. System Overview

Basic steps involved in the process are given in figure 1.As shown, input image sequence is taken from the static camera. Two consecutive frames

from the image sequence are partitioned into four quadrants. Then moving object

detection takes place after finding Normalized Cross Correlation between two partitioned

frames.

Moving Object detection in video involves verifying the presence of an object in image

sequence and possibly locating it precisely for recognition. After detecting the moving

object, the location of the moving object is obtained by performing component connected

analysis. Tracking of the detected moving object takes place by calculating the centroids

of the detected moving object. Tracking means the detection of a target over time, thus

establishing its trajectory.

The aim of object tracking is to establish a correspondence between objects or object

parts in consecutive frames and to extract temporal information about objects such as

trajectory, posture, speed and direction.



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6. Algorithm

Basic algorithm steps for the detection and tracking of moving objects are given below.

Read two consecutive frames from the image sequence called as current frame

and previous frame.

Divide these frames into four quadrants.

For ex: Current frame is divided into four parts called as x1, x2, x3 and x4.

Similarly, previous frame is divided into four parts called as y1, y2, y3 and y4.

Now find out the NCC of each sub image of current frame with the previous

frame. After this there are four values of NCC, called as c1, c2, c3 and c4.

Now find out the minimum value of NCC from these four values.

To this minimum value of NCC apply the threshold.

The threshold value is selected by taking average of four NCC values (i.e. c1,

c2, c3 and c4).

Suppose the minimum value of NCC is obtained at the first quadrant, it means

that the moving object is present in that quadrant.

Now operate in the first quadrant. Take the difference between the first

quadrants of two consecutive frames.



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Then find the location of the moving object by performing component

connected analysis and morphological processing.

Centroid calculation is done for tracking the moving object.

After this the second minimum value from the c1, c2, c3 and c4 is obtained.

This is performed to check whether any other moving object is present in

other part of the image.

If the second minimum value is also greater than threshold then it means that

the moving object is present in that quadrant. Now, identify the location of

second moving object and track that object.

Repeat the same procedure for the next frame.



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7. Performance Evaluation

Performance Evaluation for moving object detection and tracking is given in terms of

Qualitative analysis

Quantitative analysis

Time performance analysis.

7.1.1 Qualitative analysis

The most reliable approach for qualitative/visual evaluation is to display a flicker

animation, a short movie file containing a registered pair of images (I1(x), I2(x)) that are

played in rapid succession at intervals of about a second each.

Also qualitative/visual evaluation is to display the sequence to point the tracking of the

detected moving object. Moving object detection and tracking results are shown from

figure 2 to 9. Tracking results are shown by a pointer. For the sequence shown in figure

2, a person is moving from left to right. It is pointed by a star. Similarly for differentimage sequences, different pointers are used. Figure 3 shows the tracking sequence of a

car. Figure 4, 5 and 6 shows the tracking sequence of multiple objects. (i.e. fishes). In

this image sequence the shape of fish undergoes sudden deformation. (i.e. non rigid

movement). In the same image sequence repetitive motion (water) and variation in

illumination is observed.

Hence such motion is difficult to track. As observed in figure 4 SD method fails in

detecting and tracking fish sequence. But as observed in figure 6, PNCC method provides

better tracking results.



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Figure 7 shows the image sequence of a walking person. In this the walking person is

pointed by a square. As shown in figure 8, some part of the image sequence is very bright

and the other part is in the shadow. There are two moving objects. These are pointed by

square. Figure 9 shows image sequence of two moving objects. This image sequence is of

low contrast and low brightness, with two moving objects. These moving objects are

pointed by stars.



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7.1.2 Quantitative analysis

This is the process of establishing the correct answer for what exactly the algorithm is

expected to produce. [17] [18]. There are two metrics for characterizing the Detection

Rate (DR) and the False Alarm Rate (FAR) of the system. These rates, used to quantify

the output of the system, are based on:

Moving object can be detected (positive) or not detected (negative) and a decision for a

detection result can be either correct (true) or incorrect (false).

A decision for a detection result therefore will be one of these possible categories as

mentioned below. TP (true positive): detected regions that correspond to moving objects,

FP (false positive): detected regions that do not correspond to a moving object, (also

known as false alarms). FN (false negative): moving objects not detected. (also known as

misses).

These scalars are combined to define the following metrics:

DR = TP / (TP +FN) (2)

FAR = FP / (TP+FP) (3)

TP, FP and FN values for different image sequences are shown in Table 1. There are

three values for TP and FN for S2 image sequence. It means there are three moving

objects in S2 image sequence. Similarly for S4 and S11 image sequence, two moving

objects are present.

7.1.3 Time performance analysis

Temporal performances are evaluated by estimating how many CPU seconds the systemtakes to process an image of a sequence, which is the average processing time per frame.



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Table 1. TP , FP and FN of different image Sequences.

From the obtained values of TP, FP and FN, Detection rate and False alarm rate obtained,

as shown in Table 2.

Table 2. DR and FAR For Different Image Sequences.



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8. Performance of Moving Object Detection methods

The performance of all detection methods are summarized below in table 4.

Table 3 : Performance of moving object detection methods

Detection

Methods

Detecting

time/seconds

Recognition

rates

False

positives

Frame difference 38.08 92.9% 8

Background

subtraction90.34 98.4% 0

Frame difference and

Edge detection1.873 99.2% 3

Normal cross

correlation (NCC)7.65 95% 3



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9. Conclusion

An algorithm is proposed by Partitioning and Normalized Cross Correlation for thedetection and tracking of moving object from the image sequence. Important advantage

of this algorithm is that it requires very less preprocessing of the frames from image

sequence (median filtering and contrast stretching). The algorithm is robust against

changes in illumination and lighting conditions. In poor lighting conditions also the

algorithm is giving better results.

Here also we have summarized moving object detection algorithms for detecting moving

objects in video sequences from current literature. In this survey the pros and cons of the

algorithms considered and tabulated. The performance of various object detection

methods was also compared.



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10.Bibliography

1. CSI-Communications Volume No 33 Issue No 11 February 2010.

2. A Novel approach to Detect and Track Moving Object using Partitioning and

Normalized Cross Correlation by Manoj S. Nagmode, Mrs. Madhuri A. Joshi,

Ashok.M.Sapkal. .

3. A Contour-Based Moving Object Detection and Tracking by Masayuki

Yokoyama and Tomaso Poggio.

Websites

www.csi-india.org

www.icgst.com

www.IEEE.org

http://www.icgst.com/http://www.icgst.com/

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