Improved vehicle detection systems with double-layer LSTM modules

EURASIP Journal on Advances in Signal Processing

Wei-Jong Yang¹  Wan-Ju Liow¹  Shao-Fu Chen¹  Pau-Choo Chung¹  Jar-Ferr Yang¹  Songan Mao² 

[1] Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan, Taiwan;Qualcomm Incorporated, San Diego, USA;

关键词: Vehicle detection;    LSTM-based object refiner;    Spatial priority order;    Adaptive miss-time threshold;    Adaptive confidence threshold;   

DOI  :  10.1186/s13634-022-00839-6

来源: Springer

【 摘 要 】

The vision-based smart driving technologies for road safety are the popular research topics in computer vision. The precise moving object detection with continuously tracking capability is one of the most important vision-based technologies nowadays. In this paper, we propose an improved object detection system, which combines a typical object detector and long short-term memory (LSTM) modules, to further improve the detection performance for smart driving. First, starting from a selected object detector, we combine all vehicle classes and bypassing low-level features to improve its detection performance. After the spatial association of the detected objects, the outputs of the improved object detector are then fed into the proposed double-layer LSTM (dLSTM) modules to successfully improve the detection performance of the vehicles in various conditions, including the newly-appeared, the detected and the gradually-disappearing vehicles. With stage-by-stage evaluations, the experimental results show that the proposed vehicle detection system with dLSTM modules can precisely detect the vehicles without increasing computations.

【 授权许可】

CC BY