AUTOMATIC IDENTIFICATION OF ANIMAL USING VISUAL AND MOTION SALIENCY

Abstract

This paper examines the problem of detecting various types of animals in video sequence taken in the wild. Animal detection is useful in prevention of animal- vehicle accidents and will increase human and wildlife safety. We propose a fully automated method for detecting large animals before they enter the road and warn the driver through audio and visual signals, this also helps in saving crops in farm from animals. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object).To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results. Neural network is used to find the animal type.