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2.  Introduction
 Literature Survey
 Challenges
 Problem Statement
 Objectives
 Methodology
 Possible Outcome
 References

3.  In this project we present an automatic system for early smoke
source detection through the real time processing of landscape
images.
 The first part describes the segmentation technique we use to
extract persistent dynamical envelopes of pixels into the images.
We describe the temporal algorithm at the pixel level (filtering) and
the spatial analysis to bring together connected pixels into the
same envelopes (object labeling).
 The second part deals with the method we use to discriminate the
various natural phenomena that may cause such envelopes. We
describe the image sequence analysis we developed to discriminate
distant smokes from other phenomena, by extracting the transitory
and complex motions into little pre-processed envelopes.

4.  Monitoring animals in the wild without disturbing them is possible using camera
trapping framework, which is a technique to study wildlife using automatically triggered
cameras and produces great volumes of data.
 However, camera trapping collects images often result in low image quality and includes
a lot of false positives (images without animals), which must be detection before the
post processing step.
 This paper presents a two-channeled perceiving residual pyramid networks (TPRPN) for
camera trap images objection. Our TPRPN model attends to generating high-resolution
and high-quality results.
 In order to provide enough local information, we extract depth cue from the original
images and use two-channeled perceiving model as input to training our networks.
Finally, the proposed three-layer residual blocks learn to merge all the information and
generate full size detection results.
 Besides, we construct a new high-quality dataset with the help of Wildlife Thailand’s
Community and e Mammal Organization. Experimental results on our dataset
demonstrate that our method is superior to the existing object detection methods.

5.  Content-based Retrieval and Real Time Detection from Video Sequences Acquired by
Surveillance Systems.
 In this paper, a surveillance system devoted to detectabandoned objects in unattended
environments is presented to which image processing content based retrieval
capabilities have been added for making easier inspection task from operators.
 Video-based surveillance systems generally employ one or more cameras connected to a
set of monitors. This kind of systems needs the presence of a human operator, who
interprets the acquired information and controls the evolution of the events in a
surveyed environment. During the last years efforts have been performed to develop
systems supporting human operators in their surveillance task, in order to focus the
attention of operators when
 unusual situations are detected. Image sequences databases are also managed by the
proposed surveillance system in order to provide operators with the possibility of
retrieving in a second time the interesting sequences that may contain useful
information for discovering
 causes of an alarm.
 Experimental results are shown in terms of the probability of correct detection of
abandoned objects and examples about the retrieval sequences.

6.  Robust Real-Time Periodic Motion Detection
 We describe new techniques to detect and analyse periodic motion
as seen from both a static and a moving camera. By tracking
objects of interest, we compute an object's self-similarity as it
evolves in time. For periodic motion, the self-similarity measure is
also periodic and we apply Time-Frequency analysis to detect and
characterize the periodic motion. The periodicity is also analysed
robustly using the 2D lattice structures inherent in similarity
matrices. A real-time system has been implemented to track and
classify objects using periodicity. Examples of object classification
(people, running dogs, vehicles), person counting, and
nonstationary periodicity are provided.

7.  Study of Motion Detection Method for Smart Home
 Motion detection surveillance technology give ease for time-
consuming reviewing process that a normal videosurveillance
system offers. By using motion detection, it save the
monitoring time and cost. It has gained a lot of interestsover
the past few years. In this paper, a proposed motion detection
surveillance system, through the study and evaluationof
currently available different methods. The proposed system is
efficient and convenient for both office and home uses as a
smart home security system technology.

8.  Motion Detection for Security Surveillance
 This paper deals with the design andImplementation of Smart surveillance monitoringsystem using
Raspberry pi and CCTV camera. Thisdesign is a small portable monitoring system forhome and
college security. This system willmonitor when motion detected, the Raspberry Piwill control the
Raspberry Pi camera to take apicture and sent out image to the user according tothe program
written in python environment. The proposed home security system capturesinformation and
transmits it via a Raspberrytowards pc. Raspberry pi operates and controlsmotion detectors and
CCTV camera for remotesensing and surveillance, streams live records it for Future playback.
Python software plays animportant role in this project.Motion detection systems are a necessity in
themodern times. Although some people object theidea of being watched, surveillance
systemsactually improve the level of public security,allowing the system operators to detect
threats andthe security forces to react in time. Surveillancesystems evolved in the recent years
from simpleCCTV systems into complex structures, containingnumerous cameras and advanced
monitoringcentres, equipped with sophisticated hardware andsoftware. However, the future of
surveillancesystems belongs to automatic tools that assist thesystem operator and notice him on
the detectedsecurity threats. This is important, because incomplex systems consisting of tens or
hundreds of cameras, the operator is not able to notice all the events.

9. The study and analysis of Images captured by digital cameras
address a wide range of challenges, The Major Challenges Faced
are:-
 View-Point Variation.
 Occlusion=we take an overview of occlusion techniques in computer vision and
discuss how occlusion-based data augmentation techniques can be used to combat
the problem of overfitting in computer vision.
 Illumination=incident light, dome light, dark field light, and back light.
 Background Clutter= lot of objects in the image and it’s difficult for an observer to
focus their mind on any particular object.

10.  Animal Detection in boundaries is very vital
 It is critical to have a system to monitor animals intrusion and
report it to the forest offices
 Monitoring of fire in forest is at most important to save the
environment and wild life
 Tree Cutting Detection is a major concern to conserve forest

11. The Main objectives of the project are:
 To capture the image of the forest surveillance area using the
camera
 And detect if any animal or fire or fire hawk is detecting in the
surveillance area.
 Suitable action based on the type of detections happening.
 To detect Fire or fire hawks or animals and alert the nearby
government officials to prevent any destruction.

12.  Existing Technologies mainly focus on Manual Security
Systems which are not reliable and safe.
 Image Processing Techniques used are low in accuracy
 No Automated Intimation Systems are being Deployed

13. Unmanned Aerial
Vehicles(UAV)
Satellite based monitoring system

14.  Unmanned aerial vehicle based forest fire monitoring and tree cutting detection
using image processing technique :
Unmanned Aerial Vehicles(UAV) are basically drones , smaller in size. UAV
collects the images of the forest and processes those images using different
algorithms and informs if any suspicious activities are ongoing.
DISADVANTAGES:
 Limited carrying capability and area of survey
 Vulnerability to damage and rough weather
 Since the size of the drone they have less processing power
 Repairing and maintaining drones often requires specific parts. Delivering parts to
remote forest regions will take time and money
 We can overcome all these limitation by using sensor networks and Zigbee based
systems

15. EXISTING SYSTEMS
 Satellites based tree cutting and forest fire detection:
Satellites are used to capture the images of the forest . Based on the
images if there is any unusual changes are going on ,immediately
responsible department or officer will receive information.
DISADVANTAGES:
Using satellites to monitor small areas is costly and less efficient.
Weather changes can affect the accuracy of the system.
Government and space organizations co operation is needed to achieve
this. So it will be difficult to implement.

17.  HARDWARE
 System : intel i3/i5 2.4 GHz.
 Hard Disk : 500 GB
 Ram : 4/8 GB
 SOFTWARE
 Operating system : Windows XP/ Windows 7.
 Software Tool : Open CV Python
 Coding Language : Python
 Toolbox : Image processing toolbox.

18.  To Detect Intrusion in the Field
Camera data is continuously analyzed to check any change in the frame Using Some
Background Subtraction Method
 To Capture the image and Classifying Them Using Image Processing
Input from the camera is processed. Classification of image is done using Convolution
Neural Network . Classifying whether human or the animal is domestic or Wild Animal.
 Taking Suitable action based on the intruder
After Image processing and classification. If Human or Wild Animal is detected,
processor turns an alarm and intimation alert to Concerned Persons.
 To send Notification to farmers and Forest Officials
An intimation alert is sent to farmer about animal presence. We Use Twillio Messenger
to send intimation alert to farmer
 To Detect Fire in forest and Intimate
Fire Detect Fire Using Fire Sensor and intimate through Message to concerned Person

19.  Classification of Input Data into animal and Humans
 Classification of animal Type
 Intimation to concerned Person through Mail and Message

20.  Govt. of India, Department of Animal Husbandry and Dairy (2000)
 Elena Stringa and Carlo S.Regazzoni, Content-based Retrieval and
Real Time Detection from Video Sequences Acquired by
Surveillance Systems(2001).
 Shafika and Suhaimi, Outdoor wildlife motion triggered
camera(2015).
 Ross Cutler and Larry S. Davis, Robust Real-Time Periodic Motion
Detection(2012).
 Mayur J. Charadva, Ramesh V. Sejpal, Dr. Nisha P. Sarwade, A
Study of Motion Detection Method for Smart Home System(2014).
 Prof. Joshi Vilas, Mergal Bhauso, BorateRohan, Motion Detection for
Security Surveillance(2016).