Izzatullokh Makhammadjonov, Cho Seongpyo, Kim Dongsoo, Kim Beomseo
언어
영어(ENG)
URL
https://www.earticle.net/Article/A474149
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4,000원
원문정보
목차
Ⅰ. Introduction Ⅱ. Related Work 1. Deep Learning for Pedestrian Behavioral Analysis 2. LSTM and BiLSTM Architectures for Time Series Analysis 3. Pedestrian Intention Prediction and Sequence Modeling 4. Multi-Object Tracking for Temporal Sequence Construction Ⅲ. Methodology 1. Pedestrian Head and Body Detection 2. Multi-Object Tracking for Temporal Sequence Construction 3. Orientation Classification for Discrete Angle Representation Ⅳ. Experimental Results 1. Dataset Characteristics and Training Infrastructure 2. Detection and Tracking Performance for BiLSTM Input Quality 3. Observation Window Optimization and BiLSTM Performance Analysis 4. BiLSTM Architecture Performance Analysis 5. Final System Performance Ⅴ. Discussion and Anaylsis 1. BiLSTM Architecture Advantages 2. Performance Analysis and Validation Ⅵ. Conclusion ACKNOWLEDGEMENTS Reference