Deteksi Api Berbasis Data Video Menggunakan Metode Optical Flow dan Support Vector Machine

Kebakaran hutan dan lahan di Indonesia telah menjadi krisis lingkungan tahunan. Tercatat 2,6 juta hektar lahan di Indonesia terbakar pada bulan Juni hingga Oktober tahun 2015. Kebakaran menimbulkan kerusakan lingkungan dan banyak kerugian finansial utamanya apabila terjadi di pemukiman masyarakat. Maka dari itu diperlukan suatu pendeteksi adanya api untuk mendeteksi kebakaran lebih awal. Penggunaan data video untuk mendeteksi adanya kebakaran dilakukan dengan mengekstraksi berbagai karakteristik api, berupa tekstur dan pola gerak api. Maka dari itu dalam penelitian ini dilakukan pendeteksian api pada data video menggunakan ekstraksi fitur tekstur Local Binary Pattern (LBP) serta ekstraksi gerak menggunakan metode Optical Flow. Pada tugas akhir ini dilakukan segmentasi terlebih dahulu pada setiap frame video. Dari hasil segmentasi diperoleh citra potongan kandidat area api. Citra tersebut kemudian diekstraksi fitur teksturnya menggunakan LBP dan frame pada citra tersebut di ektraksi fitur geraknya menggunakan metode optical flow. Selanjutnya, kedua fitur tersebut diklasifikasi menggunakan metode Support Vector Machine (SVM). Model dievaluasi dengan menggunakan stratified k-fold untuk dipisahkan menjadi dua subset yaitu data proses pembelajaran dan data validasi/evaluasi dengan jumlah k=10. Nilai akurasi terbaik diperoleh dengan ekstraksi fitur LBP dan Optical Flow Lucas Kanade menggunakan metode SVM dengan kernel linear yaitu sebesar 95.96%, serta menghasilkan presisi sebesar 93.76% dan recall sebesar 99.73% =============================================================================================== Forest and land fires have become annual environmental crises in Indonesia. Around 2.6 million hectares were burned in June to October 2015. Therefore, an early warning system is needed to detect the presence of fire. Video data can be used to prove the existence of a fire carried out by extracting its various characteristics, consisting of the texture and pattern of fire movements. This study focuses on fire detection using video data through textures extraction of Local Binary Pattern (LBP) and motion extraction using the Optical Flow method. First, we perform a segmentation on each video frames. From this process, we obtained the image of the fire area. It will be extracted using LBP and optical flow method to obtain the texture and movement features of the fire. The features are classified using the Support Vector Machine (SVM) method. The model is evaluated using stratified k-fold to be separated into two subsets, learning process data and validation/ evaluation data with 10 number of k-folds. The best result of true positive and true negative obtained from classification using Local Binary Pattern and Lucas Kanade Optical Flow feature extraction with SVM method using linear kernel. The value of accuration is 95.96%, with precission 93.76% and recall 99.73

LBP-flow and hybrid encoding for real-time water and fire classification

The analysis of dynamic scenes in video is a very useful task especially for the detection and monitoring of natural hazards such as floods and fires. In this work, we focus on the challenging problem of real-world dynamic scene understanding, where videos contain dynamic textures that have been recorded in the "wild". These videos feature large illumination variations, complex motion, occlusions, camera motion, as well as significant intra-class differences, as the motion patterns of dynamic textures of the same category may be subject to large variations in real world recordings. We address these issues by introducing a novel dynamic texture descriptor, the "Local Binary Pattern-flow" (LBP-flow), which is shown to be able to accurately classify dynamic scenes whose complex motion patterns are difficult to separate using existing local descriptors, or which cannot be modelled by statistical techniques. LBP-flow builds upon existing Local Binary Pattern (LBP) descriptors by providing a low-cost representation of both appearance and optical flow textures, to increase its representation capabilities. The descriptor statistics are encoded with the Fisher vector, an informative mid-level descriptor, while a neural network follows to reduce the dimensionality and increase the discriminability of the encoded descriptor. The proposed algorithm leads to a highly accurate spatio-temporal descriptor which achieves a very low computational cost, enabling the deployment of our descriptor in real world surveillance and security applications. Experiments on challenging benchmark datasets demonstrate that it achieves recognition accuracy results that surpass State-of-the-Art dynamic texture descriptors