ABANDONED as input to an efficient background subtraction algorithm.

ABANDONED OBJECT DETECTOR
CAMERA SURVEILLANCE SYSTEM

 

Abstract:

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Surrendered protest identification is a
fundamental necessity in numerous video surveil-spear settings. This Paper
presented a relinquished question identification apparatus in light of an
arrangement of conceivable occasions and on an arrangement of guidelines to
follow up on those occasions. Items unfamiliar to a standard situation are
removed utilizing foundation subtraction. Video reconnaissance is a dynamic
research subject in PC vision that tries to distinguish, perceive and track
questions over a succession of pictures and it additionally makes an endeavor
to comprehend and depict protest conduct by supplanting the old conventional
strategy for checking cameras by human administrators. Foundation subtraction
includes the total distinction between the present picture and the reference
up-dated foundation over some stretch of time. A decent foundation subtraction
ought to have the capacity to beat the issue of changing brightening condition,
foundation mess, shadows, commonage, bootstrapping and in the meantime movement
division of frontal area protest ought to be done at the ongoing. It adds an extra feature
to the surveillance camera
systems. It reads video frames as input which are
currently being captured
by the camera. This frames are then given as input to an efficient background
subtraction algorithm. Background subtraction helps us to identify
if any changes
happen in the current frame
compared with the
reference frame, hence detects the foreign objects has entered
the frame. By analysing the behaviour of the object this
system warns the verified officer
about the particular object. Introducing this reduces
human effort as well as helps in giving proper awareness to the verified officer
whenever a suspicious object has been detected. It is particularly designed for security purposes in railway stations, bus stand etc.
In extension of this system,
it can also
be used in traffic
analysis systems in which improper parking, accidents, and many illegal actions
can be detected and proper actions can be taken quickly.

 

Keywords  :

 

 

 

1 .INTRODUCTION

            Deserted
question identification is a basic prerequisite in numerous video surveil-spear
settings. The framework present a relinquished question recognition device in
view of an arrangement of conceivable occasions and on an arrangement of
principles to follow up on those occasions. Articles unfamiliar to a typical
situation are removed utilizing foundation subtraction. Video observation is a
dynamic research point in PC vision that tries to identify, rec-ognize and
track questions over a grouping of pictures and it additionally makes an
endeavor to comprehend and depict protest conduct by supplanting the old
customary strategy for checking cameras by human administrators.. Terrorism is
the major treat faced in the country. Terrorism mainly focused on crowded areas
in the country. Verifying and validating these attacks and avoiding this in
future may not work as expected. There is also possibilities of unknowingly forgetting some properties of by the passenger or other persons in public or crowded area by mistake.
Surveillance of these abandoned objects or packages by security persons is very hard as recent
studies prove that an average human can make track of at-most
four object simul- taneously. Modern technology
has developed much more than we think of. So why cant we develop an advanced surveillance
system that upgrades
currently available system with lower capital investments, a system which reduces the workload of the
human and works efficiently.

Preprocessing Module not only just takes the input but also segments the video in to frames
which is again used for background subtraction. Each frame is de noised using Gaussian and Median Filters. The second Module ie. Background subtraction is done to identify the foreign object entering the frames.
Detection and Warning Module analyze the background subtracted frames and checks whether a particular foreign
object is idle for a longer period of time. Here the abnormality of object is tested by how long the object has been idle, if the object has been idle for more than an allotted time, the object is classified as abnormal object. Warning phase with in-turn marks the object in the frame and sends a warning message to the respected authority so that they can verify the condition. The Server Client module is to send the output to different users who need the live feedback of the system On The Go.

This server client system is mainly introduced so that user can access the system feedback even he/she is not near the main system. This system sends the output video feedback to server and thus other clients can access the live system
feed from anywhere within
the range of local network. Here TCP protocol along
with python socket programming is used to send data along the local network. so the Four Module are:
Preprocessing  Module
, Background Subtraction Module,
Detection and Warning Module
,Server Client Module.
This system can
be used in many situations. This system works fine
in crowded areas as well. It can
also be used in railway stations, airports, museums, banks
and much more.

 

2.RELATED WORK

            Video surveillance is an active research topic in computer vision that tries to detect,
recognize and track objects over a sequence of images and it also
makes an attempt to understand and
describe object behaviour
by replacing the old traditional method of monitoring cameras
by human operators.
Rout et.al1 describes a video system.
Background subtraction involves the ab- solute difference between the current image and the reference updated background over a period of time. It’s hard to get all these problems
solved in one background subtraction technique. So the idea was to simulate and evaluate their performance on various
video data taken in complex situations. A common approach for object detection
is to use information in a single
frame. Point detectors-Point detectors
are used  and interesting points in images which have an expressive texture in their respective localities.
A desirable quality of an interest point is its invariance to changes
in illumination and camera viewpoint. The pixels constituting the regions undergoing change are marked for fur- ther processing. This process is referred
to as the background
subtraction.

Nascimento et.al2 propose a method which rely on the ability to detect mov-
ing objects in the video stream
which is a relevant information extraction step in a wide range of computer vision applications. Each image is segmented by automatic image analysis techniques.
This should be done in a reliable and effective way in order to cope with unconstrained environments, non- stationary background and different object motion
patterns. Many algorithms have
been proposed for object detection in video surveillance applications. They rely on different assumptions e.g., statistical models of the background, minimization of Gaussian difference, minimum and maximum values,
adaptively or a combination
of frame differences and statistical background models.

V.K.Madasu et.al3
develop a system that deals with a simple
way to detect the abandoned objects.
Here the total work is divided in to different modules, each set to do specific operations or steps.
 The modules includes,
Data extraction and conversion
unit; Background subtraction module; Still-object tracking and occlusion
detection block and Alarm raising and display of result unit. A live video
stream is initially segmented into individual images from which a region of
interest is ex- tracted and converted to 3D intensity matrices (height * width
* intensity value of each pixel). The system works efficiently in normal and
little rushed environments but it fails to show its efficiency in highly
crowded environments. But as it performs much efficiently even without using
any expensive filters used for better detection rate, the small drawback of the
system can be neglected.

Medha Bharagava et.al4 proposes a paper focused on terrorism
and global security However,
the security observation framework today comprises of extensive number of
cameras, more often than not checked by a generally little group of human
administrators. The re-penny considers have demonstrated that a normal human
can just track or screen at most 3 protest at the same time. To maintain a
strategic distance from this hazard and to make this activity less demanding
and effective a mechanized observation framework must be presented.

            Chathranga Hettiarachchi et.al5 says Abandoned
object detection is a re- quirement in many video surveillance contexts They have chosen the foundation
subtraction based strategy rather than methodologies, for example, format based
following strategies. They isolated the irregularity recognition process into
two phases. The main Background Subtraction and Blob Detection is to process
pictures and change over it to helpful printed information. The second Abandoned
question identification utilizes the consequences of the primary, which are in
printed organize. Utilization of printed organize in second stage makes
programming and preparing simple.

            Oji et.al6
describes a technique which combines Affine Scale Invariant Feature Transform (ASIFT)
and a region merging algorithm to recognize objects from
images or video frames with
full boundary detection. In ASIFT algorithm the features of the objects are invariant with six different parameters namely
2 translation parameters, zoom, rotation
and 2 camera axis orientations.

            Jianning Han et.al7 develop a method to detect underwater objects or ob- stacles from a system
of sonar image by means of image processing and pattern
recognition
theory. The paper presents a novel object recognition system
using multiple invariant moments as the main feature
of the object, and the detected feature is trained by BP neural . Here a similar question is
prepared in a few pictures with various perspectives for discovering best key
purposes of it. At that point area combining calculations are utilized to
identify and perceive the protest from the picture or video frames.network with
the goal that the characterization mistake can be min-imized. The diverse
strides in-volved are: Page Setup: Margins and Layout,Pre procedures of Images
and Features Extraction Based on Multiple Invariant Moment,BP neural Network.

Ross Girshick et.al8 uses a new way of pattern recognition which is much faster than traditional SIFT and HOG algorithms in machine learning. Here they uses Fukushimas neocognitron, a hierarchical and shift-invariant model for pattern
recognition.
They have trained
this algorithm to get better
efficiency
in processing. They solve the localization problem by operating
within the recognition using regions paradigm, which has been successful for both object detection and semantic segmentation.

B. Sujith et.al9
mainly focus on how to reduce ATM crime
in the country by means of an effective image processing system that can detect
and analyses anomaly in behaviour of the persons who use ATM, and avoid the crime situation before happening.
The function of the proposed system
is incorporated with function
of ATM system. The main system has two parts:First part comprise on video camera to capture images.
Second part is a multiple object detection module which detects
the existence of more than one person in the ATM.

            P.Kuralkar et.al10
says that in computer
vision detection and tracking the moving object in video sequences is very critical task. There
are three techniques for protest following layout based, probabilistic and
pixel-wise. Pixel based strategies is one of best technique for protest
following. This technique is against the foundation between combination
strategies. In this sort of strategy, the disappointment identification and
programmed disappointment recuperation can be done adequately.    

 

 

3.PROPOSED
WORK

A . ARCHITECTURE DESIGN

            System is divided into four modules. Here live stream video from the camera is taken as the input for pre-processing phase. In pre-processing phase, the noise of the input frame
is removed. One of the best and simple
methods to do so is by smoothing the pixels. After preprocessing,
next phase just subtract the new image from the background and get the foreground objects alone. In Detection
and Warning phase it detects the idle object from the subtracted
image.From foreground images it checks whether a particular
foreign object is idle over a predefined time. The Warning phase
which in-turn marks the object in the frame and sends a warning message to the respected authority so that they can verify the condition. Next phase sends live feedback to system server, Which can
be accessed from anywhere
within the range of local network.

Figure 3.1: Architecture Design

 

Figure 3.2: Proposed Architecture Design

 

B. MODULE DESCRIPTION

Preprocessing Phase

            In pre-processing phase,
the noise of the input
frame is removed. One of the best and simple methods to do so is by smoothing the pixels. We are using two bluring methods namely Median filter and Gaussian
filter. Gaussian channel is
presumably the most helpful channel (despite the fact that not the speediest).
Gaussian separating is finished by convolving each point in the information
cluster with a Gaussian bit and afterward summing them all to create the yield
exhibit. The middle channel gone through every component of the flag (for this
situation the picture) and supplant every pixel with the middle of its
neighboring pixels (situated in a square neighborhood around the assessed
pixel).

                                                      
Figure 3.3: Preprocessing

 

 

 

Background Subtraction Phase

Figure 3.4:  Background Subtraction

Background
subtraction is a major preprocessing steps in many vision based applications. The input is taken from the webcam and the OpenCV library is used to analyze
the video. For the most part a picture’s areas of
intrigue are objects (people, autos, content and so on.) in its frontal area.
After the phase of picture preprocessing (which may incorporate picture
denoising, post preparing like morphology and so on.) protest confinement is
required which may make utilization of this system. Information video for the
most part contains a foundation and regularly more mind boggling designs. This
institutionalized picture is then passed for the discovery and acknowledgment
process. For instance, consider the cases like guest counter where a static
camera takes the quantity of guests going into or leaving the room, or a
movement camera extricating data about the vehicles and so on. In every one of
these cases, first you have to separate the individual or vehicles alone. Actually,
you have to separate the moving closer view from static foundation. On the off
chance that we have a picture of foundation alone, similar to picture of the
room without guests, picture of the street without vehicles and so forth, it is
a simple employment. Simply subtract the new picture from the foundation. We
get the closer view protests alone. Be that as it may, in the majority of the
cases, we might not have such a picture, so we have to separate the foundation
from whatever pictures we have.

The Background
subtraction utilizes two stages called foundation initialisation and foundation
refreshing. In the initial step, an underlying model of the foundation is
figured. The primary casing is considered as the main reference picture. After
a specific interim of time the reference picture will changed according to the
calculation. BS figures the frontal area cover playing out a subtraction
between the present casing and a reference show, containing the static piece of
the scene or, more when all is said in done, everything that can be considered
as foundation given the qualities of the watched scene. It turned out to be
more entangled when there is shadow of the vehicles. Since shadow is
additionally moving, straightforward subtraction will check that likewise as
frontal area. It muddles things.

Detection And Warning Phase

Figure 3.5:  Detection and Warning

In this module we detects the idle
object from the foreground frames Gen-erally
a picture’s areas of intrigue are objects (people, autos, content and so
forth.) in its forefront. After the phase of picture preprocessing (which may
incorporate picture de-noising, post handling like morphology and so on.)
question restriction is required which may make utilization of this strategy.
Foundation subtraction is a generally utilized approach for identifying moving
items in recordings from static cameras. The method of reasoning in the approach
is that of identifying the moving articles from the distinction between the
present edge and a reference outline, frequently called foundation picture, or
foundation display. Foundation subtraction is for the most part done if the
picture being referred to is a piece of a video stream.

 

Foundation subtraction gives critical
signals to various applications in PC vision, for instance reconnaissance
following or human stances estimation. In any case, foundation subtraction is
for the most part in light of a static back-ground theory which is regularly
not appropriate in genuine situations. With indoor scenes, reflections or
enlivened pictures on screens prompt foundation changes. Samy, because of wind,
rain or enlightenment changes brought by climate, static foundations strategies
experience issues with outside scenes. This is likewise used to track that
question when slight developments are caused. This recognized pixels or
territory of the computerized picture outline is given as contribution for the
caution framework. The yield gives the video record with the distinguished sit
still question in a rectangular box. A rectangular edge will be shown to
recognize the sit still protest from the foundation. At that point this casing
is passed as parameter for sit out of gear question acknowledgment. Cautioning
framework is basically used to caution the security or other approved
individual that a specific protest is sta-tionary for quite a while and
recommend to go over and check the question. This module is very useful when
the authorized person is not aware of this particular
package. The output of the system produces an alarm or a warning.
A rectangular frame will be displayed to distinguish the idle object from the background.

Server Client
Phase

 

Figure 3.6: Server Client

This module is to send the output
to different users who needs the live feedback of the system On The Go. This server client system
is mainly introduced so that user can access
the system feedback even he/she
is not near the main system. This system sends the output video feedback to server and thus other clients can access the live system feed from anywhere within the range of local network. We use TCP protocol because it the most reliable protocol available right now. Here TCP protocol along with python socket programming is used to send data along the local network.

The method used for transferring data over TCP is “Pickling”.
The pickle module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure.
Pickling is the procedure whereby a Python protest chain of command is
changed over into a byte stream, and unpickling is the opposite operation,
whereby a byte stream is changed over once again into a question pecking order.
Pickling (and unpickling) is on the other hand known as serialization,
marshaling, or leveling. We utilize Pickling on the grounds that pickle handles
unicode objects.The fundamental utilize case for pickle in Python is for
sending python information over a TCP association in a multi-center or
circulated framework (marshaling).

4 .EXPERIMENTAL RESULTS

Preprocessing

The frames of the live video stream is processed to reduce
noise. Grains in the image is removed so that the frames look more clear.

           

Figure 4.1: First Frame                                                        Figure 4.2: Denoised Frame

 

Figure 4.3: Denoised
Input Frame with Foreign object

 

Background
Subtraction

Whenever a foreign object enters the scene the object is detected and marked inside a green box. Background
subtraction is used to detect the foreign object.

                                               

                              

Figure4.4: Frame after
Background Subtraction      

  Figure 4.5:
 Frame after Thresholding

Detection and Warning

When this
object stays for
longer period of time, we consider it as abnormal condition and marks
the object inside  a red box.

        

Figure 4.6: Intermediate Output                                  Figure 4.7:
 Output of Warning
system

Server Client

Frames collected can be seen in server which is connected
in the same network as the client. Frames are taken in real time.

Figure 4.8: Frames received at server

5.CONCLUSION

This proposed
system is an abandoned object detection tool based on
a set of possible events and on a set of rules to act upon those events. System is divided into four modules. Here live stream video from the camera is taken as the input for pre-processing phase. In pre-processing phase, the noise of the input frame is removed. One of the best and simple methods to do so is by smoothing the pixels. After preprocessing, next phase just subtract the new image from the background and get the foreground objects alone.
In Detection and Warning
phase it detects the idle object from the subtracted image. From foreground images it checks whether a particular foreign object is idle over a predefined time. The Warning phase which in-turn marks the object in the frame and sends a warning
message to the respected authority so that they can verify
the condition. Next phase sends
live feedback to system server, Which can be accessed
from anywhere within the range of local network. By combining above mentioned
procedure, we are able to implement the
scenario more efficiently.

REFERENCES

1 Rout, Rupesh Kumar, “A Survey on Object Detection And Algorithm,” IEEE, 2015.

 

2 Nascimento, Jacinto, “Performance evaluation of object detection algorithms for video surveillance,” IEEE, Rovisco Pais, 2016.

 

3 V.K.Madasu, “An abandoned object detection
system based on dual background segmentation,” IEEE,
2014.

 

4Medha Bharagava, Chia-Chih Chen, “Detection of abandoned object in crouded environments,” IEEE, Austin, 2015.

 

5Chathranga Hettiarachchi, Asitha Nanayakkara, “Abandoned object detection with logical
reasoning,” IEEE, 2014.

 

6 Oji, Reza., “An automatic algorithm
for object recognition and detection based on ashift keypoints,” IEEE,
Shiraz, 2015.

 

7Jianning Han, Peng Yang,Lu Zhang , “Object Recognition System
of Image Based on Multiple Invariant Moments and BP Neural Network,”
IEEE, Taiyuan, 2016.

 

8Ross Girshick, Jeff Donahue., “Region-Based Convolutional Networks For Accu- rate Object Detection
And Segmentation,”
IEEE, 2016.

 

9B. Sujith, “Crime Detection and Avoidance
in ATM,” IEEE, 2015.

 

 

10 P.Kuralkar, V.T.Gaikwad., “Human Object Tracking Using Background Sub- straction and Shadow Removal Techniques,” International Journal of Advanced Re- search, 2015.

11 Mir S.Slmallah, Jalal.H.Awad., “Speeding up Edge Segmentation Based Mov- ing Object Detection Using Background Subtraction in Video Surveillance System,” IEEE, 2015.

 

12 Anumula, Naveen Kumar Madhuri, “Open CV Implementation
of Object Recog- nition Using Artificial Neural Networks,”
IEEE, Machilipatnam, 2016.

 

13Ibrahim, Osman, “Speed Detection Camera
System Using Image Processing Technique on Video Streams,”
IEEE, 2015.

 

14Rahna Mangalath, Anas Ghavte. 2015. Face Recognition and Tracking for En- hanced Video
Surveillance System.,
IEEE, 2015.

 

15 Abhishek Banerjee, Amit Singh, “Coloured
Object Tracking Robot Using Image Processing,” Indian
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