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

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%