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Intelligent Scene Recognition Based on Deep Learning
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DOI：10.1109/ACCESS.2021.3057075.png)
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Abstract
En 中文
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%).
Keywords:
Sensors
Acceleration
Elevators
Delays
Smart phones
Real-time systems
Gyroscopes
Low-latency
long short-term memory (LSTM)
power-consumption
real-time
scene recognition
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