A video-based vehicle counting system using an embedded device in realistic traffic conditions

One of the most important features of smart cities is efficient traffic monitoring. Currently, many monitoring approaches focus on video-processing techniques using traffic surveillance cameras. However, video analytics for traffic monitoring on edge devices like cameras is a difficult task, due to limited computational resources and variety of unknown traffic scenarios. To overcome these difficulties, we designed and evaluated a real-time vehicle counting system using deep neural networks in an embedded device. Experimental results were carried out to determine the best system configuration parameters and to analyze the impact of changing environmental conditions on our system performance. For urban vehicle counting, our approach could achieve a recall and precision values of 99% within a video processing time of 10 frames per second.