Intelligent Scene Recognition Based on Deep Learning

• Published in: IEEE access Vol. 9; pp. 24984 - 24993
• Main Authors: Wang, Sixian, Yao, Shengshi, Niu, Kai, Dong, Chao, Qin, Cheng, Zhuang, Hongcheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Classification; Classifiers; Deep learning; Delays; Elevators; Gyroscopes; High speed rail; long short-term memory (LSTM); Low-latency; power-consumption; Real time; Real-time systems; Recognition; scene recognition; Sensors; Smart phones
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3057075

Using sensor-rich smartphones to sense various contexts attracts much attention, such as transportation mode recognition. Local solutions make efforts to achieve trade-offs among detection accuracy, delay, and battery usage. We propose a real-time recognition model consisting of two long short-term memory classifiers with different sequence lengths. The shorter one is a binary classifier distinguishing elevator scene and the longer one implements a finer classification among bus, subway, high-speed railway, and others. Light-weighted sensors are employed with a much smaller sampling rate (10Hz) compared with previous works. A two-stage setting makes it robust to scenes with different duration and therefore reduces the latency of recognition greatly. Further, the real-time system refines the classification results and attains smoothed predictions. We present experiments on accuracy and resource usage and prove that our system realizes a latency-low and power-efficient scene recognition approach by trading off a reasonable performance loss (averaged recall of 92.22%).