Adaptive Image Restoration for Video Surveillance: A Real-Time Approach

One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Image degradation, often caused by factors such as rain, fog, lighting, etc., has a negative impact on automated decision-making.Furthermore, several image restoration solutions exist, including restoration models for single degradation and restoration models for multiple degradations. However, these solutions are not suitable for real-time processing. In this study, the aim was to develop a real-time image restoration solution for video surveillance. To achieve this, using transfer learning with ResNet_50, we developed a model for automatically identifying the types of degradation present in an image to reference the necessary treatment(s) for image restoration. Our solution has the advantage of being flexible and scalable.

Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads

Predicting a trip's travel time is essential for route planning and navigation applications. The majority of research is based on international data that does not apply to Pakistan's road conditions. We designed a complete pipeline for mining trajectories from sensors data. On this data, we employed state-of-the-art approaches, including a shallow artificial neural network, a deep multi-layered perceptron, and a long-short-term memory, to explore the issue of travel time prediction on frequent routes. The experimental results demonstrate an average prediction error ranging from 30 seconds to 1.2 minutes on trips lasting 10 minutes to 60 minutes on six most frequent routes in regions of Islamabad, Pakistan.