Detection of Objects in Motion—A Survey of Video Surveillance

Advances in Internet of Things, 2013, 3, 73-78

http://dx.doi.org/10.4236/ait.2013.34010 Published Online October 2013 (http://www.scirp.org/journal/ait)

Detection of Objects in Motion—A Survey of

Video Surveillance

Jamal Raiyn

Computer Science Department, Alqasemi College, Baka El Gariah, Israel

Email: raiyn@qsm.ac.il

Received August 1, 2013; revised September 4, 2013; accepted September 13, 2013

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

Video surveillance system is the most important issue in homeland security field. It is used as a security system because

of its ability to track and to detect a particular person. To ov ercome the lack of the convention al video surveillance sys-

tem that is based on human perception, we introduce a novel cogn itiv e video su rveillan ce system (CVS) that is based on

mobile agents. CVS offers important attributes such as suspect objects detection and smart camera cooperation for peo-

ple tracking. According to many studies, an agent-based approach is appropriate for distributed systems, since mobile

agents can transfer copies of themselves to other servers in the system.

Keywords: Video Surveillance; Object Detection; Image Analysis

1. Introduction

Various papers in the literature have been proposed

and focused on computer vision problems in the con-

text of multi-camera surveillance systems. The main pro -

blems highlighted in these papers are object detection

and tracking, and site-wide, multi-target, multi-camera

tracking. The importance of accurate detection and track-

ing is obvious, since the extracted tracking information

can be directly used for site activity/even t detection. Fur-

thermore, tracking data is needed as a first step toward

controlling a set of security cameras to acquire high-

quality imageries, and toward, for example, bu ilding bio-

metric signatures of the tracked targets automatically.

The security camera is controlled to track and capture

one target at a time, with the next target chosen as the

nearest one to the current target. These heuristics-based

algorithms provide a simple and tractable way of com-

puting. Conventional video surveillance systems have

many limitations to their capabilities. In one case, con-

ventional video surveillance systems have difficulty in

tracking a great number of people located at different

positions at the same time and tracking those people

automatically. In another case, the number of possible

targeted people is limited by the extent of users’ in-

volvement in manually switching the view from one

video camera to another. With cognitive video surveil-

lance system, mobile agent technologies are more effec-

tive and efficient than conventional video surveillance

systems, assuming that a large number of servers with

video camera are installed. If one mobile agent can track

one person, then multiple mobile agents can track nu-

merous people at the same time, and the server balances

the load process of the operating mobile agent on each

server with a camera.

We consider the scenario that the smart camera cap-

tures two similar objects (e.g. twin), then each object

selects a different path. The tracking process will be

confusing. Furthermore, the smart camera is limited to

cover a certain zone in public place (Indoor). Next sec-

tion introduces many solutions that have been suggested

to the above problem. The suggested solutions to im-

prove the conventional video surveillance system are

extended in various ways.

A part of the approaches is to use an active camera to

track a person automatically, and thus the security came ra

moves in a synchronized motion along with the projected

movement of the targeted person. These approaches are

capable of locating and tracking a small number of peo-

ple. Another common app roach is to position the camera

at strategic surveillance locations. This is not possible in

some situations due to the number of cameras that would

be necessary for full coverage, and in such cases, this

approach is not feasible due to limited resources. A third

approach is to identify and track numerous targeted peo-

ple at the same time involving image processing and in-

stallation of video cameras at any designated location,

J. RAIYN

since the image processing increases server load.

The limitation of human perception system in conven-

tional video surveillance system increases the demand to

develop cognitive surveillance system. Many of the pro-

posed video surveillance systems are expensive and lack

the capability of cognitive monitoring system such as no

image analysis. This makes the system lack the ability to

send warning signal autonomously in real-time and be-

fore the incidents happen. Furthermore, it is difficult and

might take a long time for people to locate the suspects in

the video after the incidents happen. The problem may

get more complete on the larger scale surveillance sys-

tem. The next generation video surveillance system ex-

pected not only to solve the issues of detection and track-

ing but also to solve the issue of human body analysis. In

the literature, it can be found many references in devel-

opment of sophisticated video surveillance system. In

this paper, we introduce the cognitive video surveillance

system (CVS). CVS aims to offer meaningful character-

istics like automation, autonomy, and real-time surveil-

lance such as face recognition, suspect objects, target de-

tection, and use of cooperative smart cameras. Many face

recognition systems have a video sequence as the input.

Those systems may require being capable of not only de-

tecting but tracking faces. Face tracking is essentially a

motion estimation problem. Face tracking can be performed

using many different methods, e.g., head tracking, feature

tracking, image-based tracking, and model-based track-

ing. These are different ways to classify these algorithms.

2. Review of Human Body Analysis

This section introduces various approaches that consid-

ered the object detection and object tracking in video

surveillance field [1-3]. The analysis of human body

movements can be applied in a variety of application

domains, such as video surveillance, video retrieval, hu-

man-computer interaction systems, and medical diagno-

ses. In some cases, the results of such analysis can be

used to identify people acting suspiciously and other un-

usual events directly from videos. Many approaches have

been proposed for video-based human movement analy-

sis [4-6].

In [7] Oliver et al. developed a visual surveillance

system that models and recognizes human behavior using

hidden Markov mod els (HMMs) and a trajectory feature.

In [8-10] proposed a probabilistic posture classification

scheme to identify several types of movement, such as

walking, running, squatting, or sitting. In [11] traced the

negative minimum curvatures along body contours to

segment body parts and then identified body postures

using a modified Iterative Closest Point (ICP) algorithm.

In addition [12,13] used different morphological opera-

tions to extract skeletal features from postures and then

identified movements using a HMM framework. Another

approach used to analyze human behavior is the Gaus-

sian probabilistic model. In [14] has been described the

real-time finder system for detecting and tracking hu-

mans. In [15] proposed a shape-based approach for clas-

sification of objects is used following background sub-

traction based on frame differencing. The goal is to de-

tect the humans for threat assessment.

In [16] presented a method to detect and track a human

body in a video. First, background subtraction is per-

formed to detect the foreground object, which involves

temporal differencing of the consecutive frames. In [17]

presented a novel approach to detect the pedestrians,

which is shown to work well in a indoor environment.

They make use of a new sensing device, which gives

depth information along with image information simul-

taneously. In [18] proposed method that deals with the

direct detection of humans from static images as well as

video using a classifier trained on human shape and mo-

tion features. The training dataset consists of images and

videos of human and non-human examples. In [19] has

been suggested to use the mobile agent for multi-node

wireless video cooperation in order to reduce redundancy

which will result repeated information collection in over-

lapping regions. In [20] introduced automatic human

tracking system based on a video surveillance system

enhanced with mobile agent technologies. In [21-23] has

been proposed a composite approach for human detection,

which uses skin color and motion information to first

find the candidate foreground objects for human detec-

tion, and then uses a more sophisticated technique to

classify the objects. Other approaches extract human

postures or body parts (such as the head, hands, torso, or

feet) to analyze human behavior.

Motion Detection

This section aims to provide the status of art of the dif-

ferent techniques of motion detection estimation. Various

studies have been introduced on the subject and the lit-

erature is very plentiful in this provin ce. We are trying to

list some methods used methods. The idea is to give an

overview of the most commonly used methods and ap-

proaches. The most used algorithms for moving objects

detection are based on background subtraction. The

background subtraction is based on comparing of the

current video frame (foreground objects) with one from

the previous frames that is called someti mes background.

3. Video Surveillance System

In this section we introduce the system model of the

video surveillance system. Video surveillance system has

been used for monitoring, real-time image capturing,

processing, and surveillan ce information analyzing.

The infrastructure of the system model is divided in

J. RAIYN 75

three main layers: mobile agents that are used to track

suspect objects, cognitive video surveillance manage-

ment (CVS), and Protocol for communication as shown

in Figure 1. Each end device, smart camera, covers a

certain zone or cell. Smart camera used for collecting

parameters of human face.

3.1. Communication Protocol

In the system model has been introduced two communi-

cation protocols. The first protocol used for agent-to-

agent protocol. Agents used this protocol for communi-

cation. The protocol is based on messages exchange as

shown in Figure 2. The goal of the protocol is to update

the agents. The second protocol is used for communica-

tion between CVS and mobile agent.

3.2. Mobile Agent Features

Mobile agents are placed in smart camera stations. Mo-

bile agent aims to track the suspect object from smart

camera station to others. Mobile agent offers various

characteristics, e.g. negotiation, making decision, roam-

ing, and cloning.

3.3. Cognitive Video Surveillance Management

Cognitive video surveillance (CVS) managed mobile

agent handoff in wireless networks. CVS provide the

mobile agent with information. Based on received infor-

mation mobile agents make decision when and where to

move to next smart camera station.

3.4. Tracking Moving Objects

In order to track moving obj ects, we introd uce two strate-

gies. The first strategy is based on messaging protocol

(msg_protocol). The goal of this msg_protocol is to in-

Figure 1. System model.

Figure 2. Agent protocol.

form the mobile agent about the position of the suspect

object. The second strategy uses the protocol to help the

mobile agent to roaming from point to others.

4. Methodology

Cognitive video surveillance (CVS) uses a data base of

images. Pixels are described by a set of binary sequences.

Each sequence presents certain properties (color). The

database is divided into two separate sets of pixels—the

training set and the test set. In both sets there are both

pixels, which belon g to a certain family of colors (attrib-

utes) and sequence, which do not belong.







,,,

TPXX XX

TNYY YY





Each image is then divided into frames, a frame being

a subset of pixel from the sequence. The number of pixel

in each frame is a variable and is dynamically set to ob-

tain optimal results.







11 1

112

22 2

212

,,,

mmm m

Xxx x

xx x





If for example a certain frame is comprised of 200

segments, the frames might consist of pixels 1 to 10, 2 to

J. RAIYN

5. Smoothing EMA

11, 3 to 12, etc. Statistical methods are then applied to

find correlation between a certain properties of the frame. In this section we introduce detection model that is based

on moving average scheme. There are three types of

moving average, that is, simple moving average (SMA),

weight moving average (WMA), and exponential moving

average (EMA). In this study, an exponential moving

average is considered. An exponential moving average

uses a weighting or a smoothing factor which decreases

exponentially. The weighting for each older data point

decreases exponentially, giving much more importance

to recent observations while not discarding the older ob-

servations entirely. The detection phase focused on the

collected data analysis. To increase the accuracy of the

forecast model, the abnormal events in the collected data

should be considered. The forecast scheme is based on

the exponential moving average. The robustness and ac-

curacy of the exponential smoothing forecast is high and

impressive. The accuracy of the exponential smoothing

technique depends on the weight smoothed factor alpha

value of the current demand. To determine the optimal

alpha factor value, fitting curve has been considered.

The basic logic of statistical differentiation of pixel is

known and widely used in many prediction systems.

1 if

0 otherwise











A large number of correlating factors is defined by

CVS and grouped in sets. A number is linked with each

correlating factor. Each factor is then turned into a single

number which represents the strength of the correlation

factors for each frame with respect to the probability that

this frame belongs to the certain family or not. As a re-

sult we have a large number of frames, for each pair of a

frame we have a number which is correlated to the prob-

ability that this frame belo ngs to a certain attribute (color

similarity) or does not belong.





1234

,,,JJJJJ

Optimization of J:



Prediction 1demand

JJ kJ

In addition to the statistical method an innovative

method of logical XOR multiplication of matrices is ap-

plied to enrich the number of frames, which are poten-

tially contributing to the prediction model.

6. Performance Analysis

We have used the object oriented programming language

C # to present the image in binary system as shown in

Figure 3. Hence Binary vectors are implemented in

WEKA platform. WEKA is stand for Waikato Environ-

ment for Knowledge Analysis. WEKA implements many

machine learning and data mining algorithms. As shown

CVS can be implemented in a dynamic environment –

when the training databases are modified the prediction

mechanism is modified as well with improved prediction

capabilities.

Figure 3. Image representation in binary system.

J. RAIYN 77

in Figures 4(a) and (b) the image analysis in visual form

is based on color classification. WEKA considers the

color of the image. The colors are represented in binary

system. WEKA clusters the binary vectors. Each cluster

represents certain attributes. As shown in Figure 5 the

comparison between simple moving average (SMA),

weight moving average (WMA) and exponential moving

average (EMA) is based on mean average error (MAE).

Furthermore we have compared the actual observations

to EMA model as shown in Figure 6. Results indicate

that all three moving average methods have more or less

similar performance in forecasting short-term times.

However, as one would expect the method using opti-

mized weights produced slightly better forecasts at a

higher computational cost. Quality of forecast is dimin-

ished as the time for which forecasts are made is farther

in the future. Moving average methods overestimate

travel speeds in slow-downs and underestimate them

when the congestion is clearing up and speeds are in-

creasing.

7. Conclusion

In this paper, we discussed several methods in the recent

literature for human detection from video. We have or-

ganized them according to techniques which use back-

(a)

(b)

Figure 4. (a) Image analysis; (b) Color classification.

0.00

2.00

4.00

6.00

8.00

MAE

SMA WMA EMA

Figure 5. Comparison bet ween MA sch emes .

Figure 6. Actual observation vs. forecasting model.

ground subtraction and which operate directly on the

input. In the first category, we have ordered the tech-

niques based on the type of background subtraction used

and the model used to represent a human. In the second

category, we have ordered the techniques based on the

human model and classifier model used. Overall, there

seems to be an increasing trend in the recent literature

towards robust methods which operate directly on the

image rather than those which require background sub-

traction as a first step. The EMA model can be used for

human behaviors prediction.

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