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1. Safety Protocols For Forest to Enhance Safety : A Machine Learning Approach

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

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.  Introduction

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. Cont …

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. Literature Survey

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. Challenges:

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 Problem Statement:

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. Aim and Objectives :

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 Existing System

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

14. EXISTING SYSTEMS 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.

16. System Architecture:

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. System Requirements

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 Methodology / Proposed System:

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

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). References: