Over the past decade, researchers have suggested many methods to find anomalies. However, none of the studies has applied frame reconstruction with Object Tracking (OT) to detect anomalies. Therefore, this study focuses on road accident detection using a combination of OT and U-Net associated with variants such as skip, skip residual and attention connections. The U-Net algorithm is developed for reconstructing the images using the UFC-Crime dataset. Furthermore, YOLOV4 and DeepSort are used for object detection and tracking within the frames. Finally, the Mahalanobis distance and the reconstruction error (RCE) are determined using a Kalman filter and the U-Net model.
Viewport prediction technologies are often used by most popular adaptive 360-degree video streaming solutions. These solutions stream only the content considered as being more likely to be watched by the final user, with the goal of reducing the volume of network traffic without compromising the user’s Quality of Experience (QoE). In this paper, we propose the Most Viewed Cluster algorithm (MVC), which is a hybrid viewport prediction method. It estimates the user viewport using two types of information: (i) the path of moving objects in the scene and (ii) the viewing behavior of previous users. Preliminary results show that MVC yields good results for long-term predictions.
Abstract A dual camera setup is proposed, consisting of a fixed (stationary) camera and a pan‐tilt‐zoom (PTZ) camera, employed in an automatic video surveillance system. The PTZ camera is zoomed in on a selected point in the fixed camera view and it may automatically track a moving object. For this purpose, two camera spatial calibration procedures are proposed. The PTZ camera is calibrated in relation to the fixed camera image, using interpolated look-up tables for pan and tilt values. For the calibration of the fixed camera, an extension of the Tsai algorithm is proposed, based only on measurements of distances between calibration points. This procedure reduces the time needed to obtain the calibration set and improves calibration accuracy. An algorithm for calculating PTZ values required for tracking of a moving object with the PTZ camera is also presented. The performance of the proposed algorithms is evaluated using the measured data.