Traffic Accident Detection Using Background Subtraction and CNN Encoder–Transformer Decoder in Video Frames

Abstract

Artificial intelligence plays a significant role in traffic-accident detection. Traffic accidents involve a cascade of inadvertent events, making traditional detection approaches challenging. For instance, Convolutional Neural Network (CNN)-based approaches cannot analyze temporal relationships among objects, and Recurrent Neural Network (RNN)-based approaches suffer from low processing speeds and cannot detect traffic accidents simultaneously across multiple frames. Furthermore, these networks dismiss background interference in input video frames. This paper proposes a framework that begins by subtracting the background based on You Only Look Once (YOLOv5), which adaptively reduces background interference when detecting objects. Subsequently, the CNN encoder and Transformer decoder are combined into an end-to-end model to extract the spatial and temporal features between different time points, allowing for a parallel analysis between input video frames. The proposed framework was evaluated on the Car Crash Dataset through a series of comparison and ablation experiments. Our framework was benchmarked against three accident-detection models to evaluate its effectiveness, and the proposed framework demonstrated a superior accuracy of approximately 96%. The results of the ablation experiments indicate that when background subtraction was not incorporated into the proposed framework, the values of all evaluation indicators decreased by approximately 3%.

Cross-Modality Interaction-Based Traffic Accident Classification

“…In this context, the probability of death occurring on the road is higher in secondary traffic accidents than in primary ones, making it crucial to quickly identify accidents on the road to prevent subsequent secondary accidents. Consequently, in the field of artificial intelligence, technologies are being actively developed to quickly detect traffic accidents or accurately classify types of accidents [2][3][4][5][6].…”

Arbitrary Timestep Video Frame Interpolation with Time-Dependent Decoding

“…In this paper, we propose a new arbitrary timestep video frame interpolation (ATVFI) neural network model with interpolation time-decoding. Generally, our method is built on an encoder-decoder framework [13]. The decoder part of our model takes the interpolation timestep t as an extra input, indicating the relative time coordinate of the desired output frame with regard to input frames.…”