Hybrid Background Subtraction in Video using Bi-level CodeBook Model Soumya Varma Federal Institute of Science and Technology, Mahatma Gandhi University, Kerala India summu121@gmail.com Sreeraj M Federal Institute of Science and Technology, Mahatma Gandhi University, Kerala India sreerajtkzy@gmail.com Abstract—Detection of Objects in Video is a highly demanding area of research. The Background Subtraction Algorithms can yield better results in Foreground Object Detection. This work presents a Hybrid CodeBook based Background Subtraction to extract the foreground ROI from the background. Codebooks are used to store compressed information by demanding lesser memory usage and high speedy processing. This Hybrid method which uses Block-Based and Pixel-Based Codebooks provide efficient detection results; the high speed processing capability of block based background subtraction as well as high Precision Rate of pixel based background subtraction are exploited to yield an efficient Background Subtraction System. The Block stage produces a coarse foreground area, which is then refined by the Pixel stage. The system’s performance is evaluated with different block sizes and with different block descriptors like 2D-DCT, FFT etc. The Experimental analysis based on statistical measurements yields precision, recall, similarity and F measure of the hybrid system as 88.74%, 91.09%, 81.66% and 89.90% respectively, and thus proves the efficiency of the novel system. Keywords— Background Subtraction,Codebook Model,Foreground Detection DCT, FFT I. INTRODUCTION Object Detection in Video has enormous applications in the field of Target Recognition, Security Surveillance, Intelligent Monitoring, Pedestrian Detection, Object Tracking etc. The identification of an object in a video could be made very easily, if the redundant video background is eliminated. In Computer Vision related applications, Background Subtraction or Filtering is a wide research area which focuses on subtracting the unessential “background area” from the “foreground region”. Any background subtraction methodology must not demand much processing time and memory usage. Codebooks are used to represent compressed form of information without demanding much processing time and memory usage. The remaining of the work is organized as follows. Section II describes the Literature Review; Section III elucidates the System Architecture. In Section IV, the details of the Implementation are explained. Results and Evaluations are described in Section V, and Conclusion and Future Scope of the work are discussed in Section VI. II. LITERATURE REVIEW The process of Background Filtering is carried out by analyzing the video frame-by-frame, maintaining a temporal continuity between the consecutive frames of a video. The approaches for Background Subtraction on the basis of processing each frame could be broadly classified as pixel- based and block-based methods. In the former approach, the pixels of a frame that provide detailed information is taken care of and decision is made on each pixel-whether it belongs to foreground or background. But in the latter approach, each frame segmented to different blocks of fixed size, is taken into account and a decision is adopted for a block so that it is either classified as foreground or background. A. Pixel based methods 1) Frame Differencing The simplest method of Background Subtraction is the Frame Differencing strategy [1], in which the pixel characteristics of a frame are subtracted from its previous frame. In this approach, a pixel is classified as foreground if the absolute difference between the pixel values in two successive frames is greater than a predefined threshold. The value for threshold must be carefully chosen to achieve accurate results. However, this method offers only coarse form of foreground which is least precise. For any two frames, frame i and frame i+1 , pixel P at position (x, y), is classified as foreground or background as follows P (x,y)= P fg if |frame i - frame i+1 |>Th (1) P bg otherwise where Th is the predefined threshold for classification. Here the value that the parameter Th is highly sensitive in obtaining the accurate output. 2) Background Modeling A background subtraction strategy compares an observed image with a background image. This process, 978-1-4799-2259-14/$31.00©2014 124