Annex 16 : automated traffic monitoring for complex road conditions

Recent advancements in computer vision and machine learning techniques have made traffic monitoring systems highly effective in well structured traffic conditions such as highways. But these systems struggle in handling complex and irregular conditions that exist in developing countries, due to lack of infrastructure and regulation. This research breaks down the problem into different sub-tasks such as vehicle detection, vehicle tracking, and vehicle recognition, then combines each process into one pipeline that can be used for traffic monitoring. Implementing the final pipeline involves improving and aggregating existing techniques. Results demonstrate the potential of these techniques for automated traffic monitoring

A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection