Towards Benchmarking Scene Background Initialization

Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.Comment: 6 pages, SBI dataset, SBMI2015 Worksho

ADVANCED MOTION DETECTION ALGORITHM FOR PATIENT MONITORING USING CELL PHONE WITH VIDEO DISPLAY

Proposed is a smart, reliable and robust algorithm for motion detection, tracking and activity analysis. Background subtraction is considered intelligent algorithms for the same. We use this to track the motion and monitor the movements of the subject in question. Mount the web camera focused to the patient. PC should have a unique external Internet IPAddress. Android mobile phone should be GPRS enabled. GSM technology is used for sending SMS. It is a client-server technology wherein client captures the images, checks for motion if any, discards the packets until motion is detected. Use background subtraction algorithm to check the motion. The surveillance camera does not move and has a capture of the static background it is facing. It uses image subtraction to determine object motion. It provides more reliable information about moving object, but it is so sensitivity to the dynamic changes such as lighting. Once motion is detected, camera stops monitoring further motion. Instead, it starts capturing the video. Simultaneously, SMS alert is sent to the responsible doctors and also alerting the medical staff with audio speaker in the hospital. Java mail API is used to mail the captured video to the entered e-mail IDs. Once the doctor demands for video, socket is established between the PC and the mobile phone and video (series of images) are streamed to the doctor’s mobile phone. Save live video of first few seconds at the server end for future use. Activate alert at the remote end

Pemodelan Latar Belakang Adaptif Menggunakan Metode Gaussian Mixture Model pada Video Dalam Air

Intisari — Aplikasi teknik pengolahan citra sudah digunakan dalam berbagai bidang kehidupan salah satunya adalah untuk proses deteksi objek. Salah satu tahap pada proses deteksi objek adalah pemodelan latar balakang. Pada penelitian ini dibuat suatu program yang dapat dugunakan untuk memodelkan latar belakang adaptif pada video dalam air menggunakan metode Gaussian Mixture Model dengan sofware Visual Studio 2010 dan Library OpenCV.Proses pada pemodelan ini adalah subtraksi model latar belakang awal dengan frame pertama yang hasilnya akan digunakan untuk mengupdate model latar belakang, begitu seterusnya hingga pada frame terakhir. Proses subtraksi dan updating dilakukan pada masing-masing intensitas piksel pada frame tersebut. Hasil pada penelitian untuk menentukan nilai β dan ρ terbaik yang nanti akan digunakan untuk proses deteksi objek. Efektifitas dari metode ini dinyatakan dengan nilai PSNR, dimana pada tiga kondisi video (pagi, siang dan malam) menunjukan nilai PSNR yang berbeda-beda yaitu 69,87 dB, 66,01 dB, dan 36,58 dB. Kata kunci : Pemodelan latar belakang, Gaussian mixture model, Visual studio

Background Subtraction Berbasis Self Organizing Map Untuk Deteksi Objek Bergerak

First part in automatic video analisys is moving object detection. An accurate moving object detection is needed indeed to next step process of automatic video analisys like tracking object detected adn then analyze of detected object. Background Subtraction is a common approach in moving object detection. The common problems in background subtraction are illumination changes, object shadow, and dynamic background like waving tree. Self organizing Maps algorithm apllied in background Subtraction to handles these common problems. Median filtering and morphological operation added after background Subtraction procces in conjunction to increase and produce accurate moving object detection. Apllied SOM, median filtering, and morphological operation in background subtraction increasing object detection accuraccy with value of MSE in 1463,73 and PSNR in 17,035 compare with alpha based background Subtraction where 4268,50 for MSE and 12,018 for PSNR

Foreground detection of video through the integration of novel multiple detection algorithims

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe main outcomes of this research are the design of a foreground detection algorithm, which is more accurate and less time consuming than existing algorithms. By the term accuracy we mean an exact mask (which satisfies the respective ground truth value) of the foreground object(s). Motion detection being the prior component of foreground detection process can be achieved via pixel based and block based methods, both of which have their own merits and disadvantages. Pixel based methods are efficient in terms of accuracy but a time consuming process, so cannot be recommended for real time applications. On the other hand block based motion estimation has relatively less accuracy but consumes less time and is thus ideal for real-time applications. In the first proposed algorithm, block based motion estimation technique is opted for timely execution. To overcome the issue of accuracy another morphological based technique was adopted called opening-and-closing by reconstruction, which is a pixel based operation so produces higher accuracy and requires lesser time in execution. Morphological operation opening-and-closing by reconstruction finds the maxima and minima inside the foreground object(s). Thus this novel simultaneous process compensates for the lower accuracy of block based motion estimation. To verify the efficiency of this algorithm a complex video consisting of multiple colours, and fast and slow motions at various places was selected. Based on 11 different performance measures the proposed algorithm achieved an average accuracy of more than 24.73% than four of the well-established algorithms. Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in this next proposed algorithm. For the said purpose the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results

A Novel Method on Video Segmentation based Object Detection using Background Subtraction Technique

Video segmentation is a process of dividing a movie into meaningful segments. It helps in the process of the detection of moving objects within a scene which play a vital role in many application such as Surveillance, Safety, Traffic monitoring and Object detection, etc., Especially, Background subtraction methods are widely used for moving object detection in videos. In this paper, a new method has been proposed for object detection using background subtraction and thresholding based segmentation algorithms.Experimental results proved that the proposed method achieved high accuracy rate than other existing techniques

Background Subtraction Berbasis Algorithma K-Means Klastering untuk Deteksi Objek Bergerak

Background subtraction menjadi bagian yang sangat penting dari deteksi objek bergerak di video. Problem utamanya adalahketepatan dalam proses menentukan objek bergerak. Makalah ini mengusulkan metode klastering dengan k-means padabackground subtraction dalam mendeteksi objek bergerak. Untuk mengevaluasi performa dari k-means digunakan MeanSquare Error (MSE) dan Peak Signal Noise Ratio (PSNR). Hasil eksperimen menunjukkan bahwa k-means mampu untukmelakukan klasifikasi piksel latar depan atau latar belakang dalam mendeteksi objek.Keyword : k-means, background subtraction, objek bergera