用向量化加速LSTM，提升轨迹识别效率
VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration
- 将LSTM输入序列通过向量化优化处理，提升计算效率
- 在146万样本上达85.57%验证准确率，训练时间减少26.2%
- 适合需要高效处理大规模轨迹数据的场景
由于轨迹数据规模庞大且需快速高效处理，活动识别面临挑战。现有方法多采用传统LSTM架构，但处理大规模数据时效率不足。为此，我们提出VecLSTM框架，通过引入向量化层，利用优化的数学运算更高效地处理输入序列。我们已将VecLSTM实现并集成至MySQL数据库中。为评估其有效性，我们在包含1,467,652个样本、七类标签的数据集上与传统LSTM模型进行对比。实验结果表明，相较于现有最优方法，VecLSTM在准确率和效率方面表现更优：验证准确率达85.57%，测试准确率为85.47%，加权F1-score为0.86；同时训练时间相比传统LSTM模型减少26.2%。
原文摘要 · Abstract (English)
Activity recognition is a challenging task due to the large scale of trajectory data and the need for prompt and efficient processing. Existing methods have attempted to mitigate this problem by employing traditional LSTM architectures, but these approaches often suffer from inefficiencies in processing large datasets. In response to this challenge, we propose VecLSTM, a novel framework that enhances the performance and efficiency of LSTM-based neural networks. Unlike conventional approaches, VecLSTM incorporates vectorization layers, leveraging optimized mathematical operations to process input sequences more efficiently. We have implemented VecLSTM and incorporated it into the MySQL database. To evaluate the effectiveness of VecLSTM, we compare its performance against a conventional LSTM model using a dataset comprising 1,467,652 samples with seven unique labels. Experimental results demonstrate superior accuracy and efficiency compared to the state-of-the-art, with VecLSTM achieving a validation accuracy of 85.57\%, a test accuracy of 85.47\%, and a weighted F1-score of 0.86. Furthermore, VecLSTM significantly reduces training time, offering a 26.2\% reduction compared to traditional LSTM models.
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