MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model

Abstract

With the ever-increasing intelligent perception capability of mobile terminals, the demand for fine-grained human activity recognition has become urgent. Transportation mode recognition, as a special branch of human activity recognition, plays a vital role in various mobile smart services. Some studies have been conducted on attitude-independence transportation mode recognition from various perspectives. However, it is challenging to recognize cross-place transportation mode due to the diversity of human activity and carrier deployment place. Towards this end, we propose a robust cross-place transportation mode recognition algorithm, which consists of three parts: Multi-Sensor Neural Network model, a variant bootstrap (ensemble learning) method, and data augmentation. The Multi-Sensor Neural Network model leverages multiple SF-SEDNets to extract spatial-temporal and spectral fusion features from different sensor combinations. The proposed data augmentation method addresses the unbalance data problem without introducing additional data collection costs, and the variant bootstrap method improves the robustness of our proposed algorithm. We evaluate our proposed algorithm on the Sussex-Huawei Locomotion-Transportation recognition challenge 2019 dataset. Extensive experimental results indicate that our algorithm achieves 81.45% macro-F1 score on the test dataset, which is 3.03% higher than that provided by the winning entry of this competition and is 14.85% higher than that of the official baseline.