International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 02 | Feb -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 995 A Hybrid Auto Surveillance Model Using Scale Invariant Feature Transformation for Tiger Classification G.Raghavendra Prasad 1 1 Assistant Professor, Amity School of Engineering & Technology, Amity University Chhattisgarh ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this paper a Schematic Model is proposed to classify Tiger with a new hybrid algorithm. For effective biodiversity monitoring over recent years Image Sensors are increasingly deployed wild life species such as tiger is on the brink of being extinct species throughout the world. It is a tedious work to perform classification of animals due to various natural constraints and occlusions. In order to perform that systematic approach like segmentation, feature extraction and classification they are to be followed by better pattern recognition systems. Mathematical interpretations are given and the local features are extracted using scale invariant feature transformation (SIFT) and linear SVM to classify image of species. The images stored in the database are compared with the incoming data set with the feature vectors and the decision is made whether the identified image belongs to that specific Tiger or not. Key Words: SIFT, SVM , KNN, Hybrid System, K Clusters 1. INTRODUCTION The classification and recognition of Tiger will be helpful in automated surveillance systems. The tagging / labeling will be useful for tracking migrating Tiger across the woods. A model to recognize and classify the same will be enrooting to a very good expert system. In order to design a real time based recognition system with better object recognition capability the objects should be projected and characterized in best possible manner. For Real time based situations in wild life scenarios classifiers such as SVM is used for its acclaimed fast testing ability and a very good precision rate. Object characterization can be achieved by visual descriptors, shape descriptors or texture representation. Animal classification or fine-grained animal recognition [1]. Profiling sensors are electro-optical sensing devices which will help capture moving Tigers accurately in woods. The data acquired from a detector in the profiling sensor are extracted and used to create a profile or two-dimensional outline of the Tiger. Classifications of objects in a deeper sense [4]. 1.1 Pattern Recognition Automation To identify and to collect and analyze information from the jungle where there exists a necessity to separate the foreground details from the background the pattern recognition system should be precise and in order to achieve the same on mechanizing these abilities with the goal of automating the identification process is essential. Automated pattern recognition systems use algorithms to process data collected through image sensors resulting in an identification of the group of which the data are most representative [7]. Three activities are considered to be most important for the converting the data collected to get identified as feature. They are preprocessing, feature extraction and feature selection. The output of the above description process will be containing a set of features called as feature vector. A Hybrid pattern recognition approach is mooted for the objective Fig 1: Automated PR System Fig 2: PR Algorithm Design Schema