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International Conference on Computing, Communication and Automation (ICCCA2017)
Wild Animal Detection using Discriminative Feature-oriented Dictionary Learning Pragya Gupta
Gyanendra K. Verma
Dept. of Computer Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana (India) pragyagupta10@gmail.com
Dept. of Computer Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana (India) gyanendra@nitkkr.ac.in
Abstract— Wild animal detection is an active research area since last many decades among wildlife researchers to study and analyze wild animals and their behavior. This paper presents sparse representation based wild animal detection system using Discriminative Feature-oriented Dictionary Learning (DFDL). DFDL extracts discriminative class-specific features and shows a low complexity method for animal detection. We acquired classspecific dictionaries allowed to represent a new image to identity of the class of the image. Concurrently, these dictionaries are incapable of representing the samples of other classes. The experiments are performed over in-house database compiled by us. We achieved promising results using DFDL with 93% accuracy. Keywords— Animal Detection; Dictionary Learning; Feature extraction
I.
INTRODUCTION
Today’s, world has made computers an inseparable part of their life as computers are used to perform all the work of humans with better accuracy and efficiency. We aim to increase the computation power and capabilities of the computers in order to perform more intelligent tasks with the help of computers. One of the tasks is analysis and interpretation of visual scenes by computers. Visual scene analysis is a high-level tasks that acquire knowledge from videos or digital images, which comes under the domain of computer vision. Computer vision deals with image data (such as digital images, a sequence of images, multi-view images, etc.) and information to form decisions. Detection of objects from different scenes is a prime requirement for various computer vision applications. Human visual system is one such example which can easily detect and recognize one class of object from another class of object. Object detection is widely used for automatic analysis of digital data, Human-Computer Interaction (HCI), automated processes, smart vehicles and wild animal detection. Wild animal detection helps wildlife researchers to analyze and study wild animal habitat and behavior. Wild animal detection is a challenging problem due to high intra-class variation among every animal. Other challenges are color variations, pose or view angle differences, partial occlusion, illumination variations and background clutter. The paper discusses the problem of object detection, particularly in
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respect of detection of wild animals and presents our approach to developing the framework for Wild Animal Detection. The use of animal detection varies from user to user. For example, it is beneficial for the researchers who want to detect animals, analyze and study their behavior. The study of the animals’ behavior is the life work of many people. But doing this task manually is a critical task, hence an automated animal detection with further use of object tracking systems can help such researchers to analyze these animal’s behavior. Also, animal detection can be a line of defense against a canine. The automation of animal detection faces challenges as we can find there is a huge intra-class variation among every animal. Also, we need to detect animals irrespective of the color variations, pose or view angle differences, partial occlusion, illumination variations and background clutter. In this paper, we propose a framework for wild animal detection using DFDL (Discriminative Feature-oriented Dictionary Learning). DFDL method is used to acquire class specific dictionary that allow representing a new image to the identity of the class of the image. Concurrently, these dictionaries are incapable of representing the sample of other classes [1], [2]. The paper is organized as follows. In the next section, a brief literature survey is given. In Section 3, we describe our methodology used. In Section 4, we report and analyze the experimental results. Finally, we conclude with conclusion in Section 6. II.
LITERATURE SURVEY
Object Detection is a field of computer vision and image processing that involves detecting objects of varying class (like animal, humans or cars) present in images and videos. Some well-researched applications of object detection are in the domain of car detection, face detection, image retrieval and video surveillance. In this section, we give a brief review of the existing literature in the field of animal detection. Zhang et al. [3] implemented a method of adaptive change detection using an estimation model based on a threshold for automatic object segmentation from a video and an edge model using canny edge detection for the video object. Ramanan and Forsyth [4] attempts to build appearance models of animals on the basis of the temporal coherency. It uses spatial model for each cluster.
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International Conference on Computing, Communication and Automation (ICCCA2017) MJ. Wilber et al. [5] designed a framework for animal recognition which was focused on protection of endangered wild species in the Mojave Desert. X. Li et al. [6] an interesting approach for efficient tracking is shown using the Kalman filter for multiple objects moving and creating a state of confusion.
exclusively extracting features for classification purpose. The method emphasizes on the construction of dictionaries that demonstrates huge inter-class differences while small intraclass differences, giving improved classification performance. The framework is based on sparsity-constrained optimization problem. A description of sparse representation is as follows:
R. N. Hota et al. [7] explores the various shape features and studies it in terms of accuracy and performance metrics. The problem of moving object detection from moving cameras is addressed by a typical method which is the extension of background subtraction [8]. Guo Lihua [9] an approach of background and foreground segmentation is used for low time complexity of automated video segmentation. They combined the color analysis module with motion analysis module. T. Burghardt [10] uses HAAR-Adaboost algorithm where features are extracted through HAAR and features are classified through AdaBoost classifier. Kanade Lucas Tomasi algorithm is used for tracking the regions on the face. Weiwei Zhang et al. [11] have detected the animal head by using shape and texture features. They are captured through a combination of HAAR and HOG features creating a gradient oriented feature set. David Forslund et al. [12] an efficient classification approach based on a cascade boosting is used, the boosting approach is used to deal with varying computationally complex features. Abdelhamid Mammeri et al. [13] have proposed a two stage system that performs LBPAdaboost algorithm in first stage and HOG-SVM algorithm in second stage. Zhi Zhang et al. [14] is a patch verification method which uses ensemble graph cut and SVM to identify the animal in an image.
A sparse approximation is a sparse vector that solves a system of equations approximately. Let a linear equation y=A× x, where A is an undetermined m × n matrix (m
