Accident Recognition via 3D CNNs for Automated Traffic Monitoring in Smart Cities

© 2020, Springer Nature Switzerland AG. Automatic recognition of road accidents in traffic videos can improve road safety. Smart cities can deploy accident recognition systems to promote urban traffic safety and efficiency. This work reviews existing approaches for automatic accident detection and highlights a number of challenges that make accident detection a difficult task. Furthermore, we propose to implement a 3D Convolutional Neural Network (CNN) based accident detection system. We customize a video game to generate road traffic video data in a variety of weather and lighting conditions. The generated data is preprocessed using optical flow method and injected with noise to focus only on motion and introduce further variations in the data, respectively. The resulting data is used to train the model, which was then tested on real-life traffic videos from YouTube. The experiments demonstrate that the performance of the proposed algorithm is comparable to that of the existing models, but unlike them, it is not dependent on a large volume of real-life video data for training and does not require manual tuning of any thresholds

Domain Adaptation for Car Accident Detection in Videos

© 2019 IEEE. In this paper, we implement a deep learning model for car accident detection using synthetic videos while adapting the model, using domain adaptation (DA), to real videos from CCTV traffic cameras. The synthetic data are rendered using a video game. The reason to use such data is the lack of real videos of car crashes from CCTV. Though a video game may allow us to generate car crashes in a variety of scenarios, the distinction in synthetic and real videos can negatively affect the model\u27s performance. Accordingly, our aim is three-fold: render numerous synthetic videos having significant variations, train a 3D CNN based deep model on the collected videos, and use DA to adapt the model from synthetic to real videos. Our experimental results, obtained under a variety of experimental setups, demonstrate the feasibility of using our approach for car accident detection in real videos

Nucleophilic Substitution Reactions in the [B3H8]− Anion in the Presence of Lewis Acids

As a result of our study on the interaction between the octahydrotriborate anion with nucleophiles (Nu = THF, Ph3P, Ph2P-(CH2)2-PPh2 (dppe), Ph3As, Et3N, PhNH2, C5H5N, CH3CN, Ph2CHCN)) in the presence of a wide range of Lewis acids (Ti(IV), Hf(IV), Zr(IV), Al, Cu(I), Zn, Mn(II), Co(II) halides and iodine), a number of substituted derivatives of the octahydrotriborate anion [B3H7Nu] are obtained. It is found that the use of TiCl4, AlCl3, ZrCl4, HfCl4, CuCl and iodine leads to the highest product yields. In this case, it is most likely that the reaction proceeds through the formation of an intermediate [B3H7-HMXnx], which was detected by NMR spectroscopy. The structures of [Ph3P·B3H7] and [PhNH2·B3H7] were determined by X-ray diffraction