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.
Accident detection is one of the biggest challenges as there are various anomalies, occlusions, and objects in the image at different times. Therefore, this paper focuses on detecting traffic accidents through a combination of Object Tracking (OT) and image generation using GAN with variants such as skip connection, residual, and attention connection. The background removal techniques will be applied to reduce the background variation in the frame. Later, YOLO-R is used to detect objects, followed by DeepSort tracking of objects in the frame. Finally, the distance error metric and the adversarial error are determined using the Kalman filter and the GAN approach and help to decide accidents in videos.