Deep neural network-based physical distancing monitoring system with tensorRT optimization

During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies

Road images augmentation with synthetic traffic signs using neural networks

Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector

CCTSDB 2021: A more comprehensive traffic sign detection benchmark

Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance