Silhouette coverage analysis for multi-modal video surveillance

In order to improve the accuracy in video-based object detection, the proposed multi-modal video surveillance system takes advantage of the different kinds of information represented by visual, thermal and/or depth imaging sensors. The multi-modal object detector of the system can be split up in two consecutive parts: the registration and the coverage analysis. The multi-modal image registration is performed using a three step silhouette-mapping algorithm which detects the rotation, scale and translation between moving objects in the visual, (thermal) infrared and/or depth images. First, moving object silhouettes are extracted to separate the calibration objects, i.e., the foreground, from the static background. Key components are dynamic background subtraction, foreground enhancement and automatic thresholding. Then, 1D contour vectors are generated from the resulting multi-modal silhouettes using silhouette boundary extraction, cartesian to polar transform and radial vector analysis. Next, to retrieve the rotation angle and the scale factor between the multi-sensor image, these contours are mapped on each other using circular cross correlation and contour scaling. Finally, the translation between the images is calculated using maximization of binary correlation. The silhouette coverage analysis also starts with moving object silhouette extraction. Then, it uses the registration information, i.e., rotation angle, scale factor and translation vector, to map the thermal, depth and visual silhouette images on each other. Finally, the coverage of the resulting multi-modal silhouette map is computed and is analyzed over time to reduce false alarms and to improve object detection. Prior experiments on real-world multi-sensor video sequences indicate that automated multi-modal video surveillance is promising. This paper shows that merging information from multi-modal video further increases the detection results

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach

Pengembangan Algoritma Robust Untuk Menghitung Kendaraan Bergerak Berbasis Pengolahan Citra

Permasalahan lalu lintas merupakan salah satu permasalahan yang dihadapi di kota-kota besar pada umumnya. Hal ini dikarenakan meningkatnya jumlah volume kendaraan sehingga berpotensi menimbulkan kemacetan. Oleh karena itu diperlukan analisis kepadatan lalu lintas untuk mendapatkan informasi yang dibutuhkan seperti banyaknya kendaraan yang melintas, yang mana informasi tersebut nantinya akan dapat digunakan oleh pihak terkait sebagai pertimbangan pengaturan traffic light, pelebaran jalan, ataupun kebijakan-kebijakan lainnya. Salah satu metode yang dapat diterapkan adalah dengan cara melakukan counting berbasis pengolahan citra digital yang lebih efisien dari proses counting secara manual. Penelitian tentang counting berbasis pengolahan citra telah banyak dilakukan sebelumnya, namun beberapa terkendala waktu pengambilan video. Maka dari itu pada penelitian ini dibahas pengembangan algoritma robust terhadap video pagi, siang, dan sore untuk menghitung kendaraan pada video lalu lintas. Proses seperti background subtraction, noise removal, object detection dan counting akan dibahas didalamnya. Pada proses background subtraction digunakan metode GMM (Gaussian Mixture Model). Proses robust yang dilakukan adalah dengan cara melakukan proses updating foreground dari hasil proses GMM yang diperoleh, hal ini dilakukan untuk mendeteksi bayangan yang bergerak menyerupai objeknya. Proses tersebut dilakukan dengan cara melakukan pengecekan terhadap piksel pada suatu koordinat citra foreground yang bernilai 1 (terdeteksi sebagai foreground) dan dilakukan proses seleksi berdasarkan nilai mean dan modusnya pada intensitas frame saat itu. Pada object detection akan diperiksa tetangga pada tiap-tiap piksel image. Hasil yang diperoleh menunjukkan bahwa proses updating foreground memperoleh hasil yang lebih akurat dari proses GMM. ======================================================================================================= The traffic problems are faced almost in every big city. This is due to the increase number of vehicles that potentially cause a traffic jam. Therefore, it is necessary to analyze the traffic density to obtain information, such as the number of passing vehicles, that information will become refference for taking some policies like traffic light time setting, road widening or the other policy. One of methods than can be implemented is by counting vehicle based on digital image processing, that more efficient than the manual counting. Research about image processing for counting has been done, but some of it depend on the video time taken. This research discussed about an improved robust algorithm for counting vehicle in a traffic video for morning, afternoon and evening video. Processes such as background subtraction, noise removal, object detection and counting will be discussed therein. In the background subtraction method is used GMM (Gaussian Mixture Model), then the obtained result from GMM method will be processed in order to remove shadow that move similarly like an vehicle based on its intensity with frame mean and mode. While in object detection will be checked neighbors on each pixel of the image. Robust process was carried out by analyzing shadow that detected in GMM process, then shadow removal will be executed in order to obtain detecting and counting results that was accurated, this process called updating foreground process. Updating foreground process will update the obtained result from GMM depend on its mean and mode on present frame. The obtained result show that updating foreground result is more accurate than GMM